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Analysing the link between public transport use and airborne transmission: mobility and contagion in the London underground

Environmental Health201817:84

  • Received: 17 July 2018
  • Accepted: 13 November 2018
  • Published:
Open Peer Review reports

The transmission of infectious diseases is dependent on the amount and nature of contacts between infectious and healthy individuals. Confined and crowded environments that people visit in their day-to-day life (such as town squares, business districts, transport hubs, etc) can act as hot-spots for spreading disease. In this study we explore the link between the use of public transport and the spread of airborne infections in urban environments.

We study a large number of journeys on the London Underground, which is known to be particularly crowded at certain times. We use publically available Oyster card data (the electronic ticket used for public transport in Greater London), to infer passengers’ routes on the underground network. In order to estimate the spread of a generic airborne disease in each station, we use and extend an analytical microscopic model that was initially designed to study people moving in a corridor.

Comparing our results with influenza-like illnesses (ILI) data collected by Public Health England (PHE) in London boroughs, shows a correlation between the use of public transport and the spread of ILI. Specifically, we show that passengers departing from boroughs with higher ILI rates have higher number of contacts when travelling on the underground. Moreover, by comparing our results with other demographic key factors, we are able to discuss the role that the Underground plays in the spread of airborne infections in the English capital.

Our study suggests a link between public transport use and infectious diseases transmission and encourages further research into that area. Results could be used to inform the development of non-pharmacological interventions that can act on preventing instead of curing infections and are, potentially, more cost-effective.

  • Public transport
  • Crowd modelling
  • Underground
  • Influenza

Epidemic outbreaks have always been present throughout human history, affecting peoples day-to-day life. Having an improved knowledge of infectious disease spreading mechanisms and control measures is pivotal for humanity’s well-being. Furthermore, gaining a better understanding of how interactions between infective and healthy individuals lead to contagion is a crucial step in that direction. The idea behind our work arose from the necessity of creating more realistic models describing the spread of infectious diseases that could be used for policy making in order to have an impact on a population’s daily life. As an example, in the specific case of influenza, UK Department of Health, created the 2011 UK Influenza Pandemic Preparedness Strategy [1] where effective response to a pandemic are set out. The document reports that there is not enough evidence that restrictions on mass gatherings will have any significant effect on influenza virus transmission. Furthermore, from the literature and available data, there is no conclusive evidence of the individual effect of restrictions of mass gatherings to help reduce influenza transmission. As a consequence, the UK Government’s position on large public gatherings, crowded events travelling and public transport use is not only neutral in light of lack of evidences, but those types of events are even encouraged because they represent an important indicator of ’normality’ and may help maintain public morale during a pandemic [1].

When studying the spread of airborne infections on a metropolitan scale there are some environments that serve as seeds of epidemic spread, where a higher number of individuals get in contact with each other. Previous work [2] has shown that incorporating elements of pedestrian modelling [39]) when studying the spread of a generic airborne infection in crowded environments, can greatly improve the model’s fidelity. Combining elements of traditional compartmental models [10, 11], network models [12, 13] and wearable sensor based studies [1416], an analytical method was recently devised [2] with the aim to be able to more accurately model small scale scenarios (i.e. pedestrian interaction). Here we apply this mathematical description to the particular case of the London Underground network by inferring a passenger transport model from data obtained from the TfL (Transport for London) [17].

Since it opened in 1863, the London Underground has become the most important transport network of the English capital and is considered the oldest rapid transit system in the world. It serves 270 stations, has 402 km of extension and carries a number of 1.265 billion annual passengers. Therefore, its stations constitute an ideal use case of crowded and confined environments and can be analysed while studying crowd dynamics and contagion mechanisms.

In the first part of our work we mathematically derive, from the TfL dataset, the time individuals take to move in a system of two stations connected with each other. Furthermore, we show how this model can be extended to a whole line of the London Underground and, from this, we evaluate the number of contacts and new infections in some selected stations. In the second part, we use real data on influenza-like-illnesses (ILI) collected by NHS from GPs in London boroughs and show the correlations between the use of the underground and new ILI infections.

Previous work [2] has studied the number of new infections created when infective individuals (along with susceptible ones) move in a corridor. Instead of completely modifying the compartmental model framework, the attention was focused on the transmission rate that is generally considered constantly equal to λ=cβ in the traditional approach, where β is the contact rate and c represents the proportion of contacts that end up becoming infected. In the new definition fully explored in [2] it is shown how, from an individual-level perspective, the contact rate β can be derived from the average local density around the infective individual 〈ρ〉, which can be estimated statistically from the average density within a larger space ϱ, using first an Eulerian description of the crowd density Gamma distribution \(p^{s}(\rho ;A;B)=\frac {B^{A}}{\Gamma (A)}\rho ^{A-1} e^{-B\rho }\) where A=3μ=3ϱ=3N/(Lw) and then re-formulated into a Lagrangian description \(p^{t}(\rho ; A; B) = \frac {p^{s}(\rho ; A; B)}{\langle v(\rho)\rangle }\). Since \(0.05 \le \langle v(\rho)\rangle = \frac {1/\sqrt {\rho } - 1/\sqrt {\rho _{\text {max}}}}{0.5} \le 1.34\) is the average velocity of a pedestrian, the average local density around an individual can be formulated as:
$$ \langle \rho \rangle = \int_{0}^{\infty} \ p^{t}(\rho)\rho\, d\rho $$
Moreover, when breathing, large droplets are carried up to 1 meter (2 meters when coughing and 6 meters when sneezing) [1820]. Susceptible individuals can become infected by inhaling these droplets thus, the density-dependent transmission rate can be re-defined as:
$$ \lambda^{\text{density-dependent}} = c \langle \rho \rangle \pi R^{2} $$

where πR2 is the area that surrounds the infective individual, according to a radius R meters which represents the maximum distance large droplets can be carried to. Consequently, in order to calculate the number of infections produced by an individual walking in a corridor, we need to calculate the average density of the whole corridor.

When considering the path that individuals walk inside a station, we can visualise it as a corridor from the entrance of the station to the train platform (and vice versa). If we can estimate the average density inside that station, at any time of the day, we can infer the local density and, consequently, the transmission rate, thus obtaining the number of new infections occurring in that station throughout the day. We know that the average velocity of individuals moving in the station is v=D/T, where D is the distance walked by the individual from the entrance to the train (or from the train to the exit) within a duration of T seconds. Moreover the average velocity as \(v(\varrho) = \frac {\frac {1}{\sqrt {\varrho }} - \frac {1}{\sqrt {\varrho _{\text {max}}}}}{0.5}\) is derived in [4] from a microscopic model of pedestrian flow, and is defined considering the difference between the net distance between individuals and the mean distance between their center of masses over the net-time headway, and where ϱmax is the maximum value of the density. Thus, we can infer the average density ϱ by knowing the time T the individuals take to walk inside the station having v(ϱ)=D/T and obtaining
$$ \varrho = \frac{4 \varrho_{max}}{\left(\frac{D}{T} \sqrt{\varrho_{max}}+2\right)^{2}} $$

This means that, in order to solve the epidemiological problem of the number of new infections occurring in the stations of the network, we need to initially solve a pedestrian dynamics model allowing us to determine the time individuals take to walk inside the station at different times of the day.

In the first week of November 2009, TfL collected data from the Oyster cards (the electronic ticket used on public transport in Greater London) and made around 10% of them available to the public. When people use the underground, they tap their Oyster card once at the entrance and once again at the exit. These journeys were sampled randomly and the time stamps and Oyster card ID is reported for each entry and exit. TfL also provides a 100% sample of the total average number of entries to and exits from all the stations, every fifteen minutes. Using these data sets, we develop a method to infer the time a passengers take to walk across their starting and arrival stations. By calling A the starting station of a generic journey and B the arrival station, we can consider the total journey time as a sum of smaller trips.

The total journey time for a single passenger ΔTA−>B=

the time required to walk across the starting station from the entrance to the platform \(\Delta t^{A}_{en}\) +

+ the waiting time on the platform \(\Delta t^{A}_{P} \)

+ the travel time the train takes to go from A to B, ΔtA−>B (for simplicity we do not consider trips that require a change of line)

+ the time to walk across the arrival station from the platform to the exit \(\Delta t^{B}_{ex}\).
$$ \Delta T_{A->B} = \Delta t^{A}_{en} + \Delta t^{A}_{P} + \Delta t_{A->B} + \Delta t^{B}_{ex} $$

Our goal is to find an estimation of the average density in the station ϱ=N/Lw (where L and w represent respectively length and width of the walked path). Since this number is clearly not constant during the day (from the station opening until its closure) because the number of people using the station N varies throughout the day, we study the average density in a time interval of 15 min and reiterate the process throughout the day. In order to do so we look at the data of the journeys in that specific time interval.

The Oyster card provide only enough data to infer ΔTA−>B, while the remaining terms of Eq. (4) are unknown. To solve this problem we use data collected by a train tracking system (developed at CASA [21]) which reports the exact position of each train of the network every 3 min. By knowing the positions of all the trains in the network we are able to infer the time the train takes to move from one station to the next, and the average waiting time at the platform \(\left (\Delta t^{A}_{P} + \Delta t_{A->B}\right)\). The still analytically unsolvable equation can now be solved computationally using the method of least squares to calculate the walking time across each of the two stations.

As an example, we show here the solutions obtained when studying the Central Line (49 stations and 74km of extension). For practical reasons we show here curves for only a few of the involved stations (Fig. 1) and report in Table 1 the correlation coefficients for the other Central line stations.
Fig. 1
Fig. 1

Correlation coefficients for four stations on the Central line. We analysed all Central line trips arriving and departing in a given station (Liverpool Street n=14723; Notting Hill Gate n=5058; Marble Arch n=4102; Stratford n=8426). Results show a peak configuration highlighting the fact that people take more time to traverse the stations during specific times of the day. The correlation coefficient between the time necessary to walk from the entrance of the station to the platform (and viceversa) and the maximum number of individuals in the station at a specific time (data provided by TfL on the number of people entering and leaving each stations every fifteen minutes) show that these times are strictly connected to the density in the station, meaning that more crowded is an area the longer it will take to traverse it. The walking times are multiplied by 5, 10 or 20 for display purposes

Table 1

Correlation Coefficients between the times required to traverse the station and the cumulative number of passengers entering and exiting the station in a 15-min period

Correlation coefficient

Total number of trips (n)

Bethnal Green

Bond Street

Buckhurst Hill

Chancery Lane

East Acton

Gants Hill

Hanger Lane

Holland Park

Lancaster Gate

Liverpool Street

Marble Arch

Mile End

Newbury Park

North Acton

Notting Hill Gate

Oxford Circus

Ruislip Gardens

South Ruislip

South Woodford

St. Paul’s

Tottenham Court Road

White City

Results are shown for most of the Central Line stations (μ=0.76178,σ=0.13204). Since TfL provides a 10% sample of travels happening during a single week, of which we analyse only the ones happening during weekdays and not involving a change of line, data points for some of the less busy stations were not available or available only during peak times translating into a lower correlation coefficient. In general, a higher amount of data points ensures a more accurate correlation coefficient value. Note that we have excluded stations that had less than N trips in any one 15-minute segment, where N is the average number of trips (i.e. passengers) per station given by ratio between the total number of trips departing(arriving) from the selected station and arriving(departing) in one of the other stations on the line, over the number of station in that line

Figure 1 highlights two important consequences arising by the use of this method: (i) the presence of peaks and (ii) the correlation between the curves.

Firstly, the model is able to capture the expected bi-modal behaviour: the morning and the afternoon peaks, meaning that, at the times when the stations are more crowded i.e. around 9 am and 6 pm when people travel to/from work it takes longer to traverse the stations. Moreover by comparing the Time Walked curve (i.e. the curve that represents the time it takes each moment of the day to cross the selected station) with the curve given by the maximum number of individuals present in the station during the day we observe a high correlation coefficient between the two. Consequently, we can say that the method captures the fact that the more crowded a station is, the longer it will take to walk through it.

From the times required to walk inside each stations we can determine the transmission rate defined in Eq. 2. Finally the number of new infections that arise from the contacts happening inside each single station during the whole day can be calculated solving the simple compartmental model described in [2]
$$ \begin{array}{rl} {\dot s} =& - \lambda i s \\ {\dot e} =& \lambda i s \\ {\dot i} =& 0 \\ \end{array} $$

Where s and i are the compartments representing susceptible and infectious individuals respectively and e is the compartment of exposed (infected but not infectious) individuals.

Influenza-like illnesses

The theoretical description of the model has been presented in relation to a generic airborne human infection. In order to test the applicability of the model in a realistic context, we will now focus our attention on Influenza-like illnesses (ILI). ILI is described by the Centers for Disease Control and Prevention (CDC) as a nonspecific respiratory illness characterized by fever, fatigue, cough, and other symptoms. The majority of ILI cases is not caused by influenza viruses but by others such as rhinoviruses and respiratory syncytial virus (RSV) adenoviruses, and parainfluenza viruses. ILI infections can lead to serious complications and require hospitalization. Moreover individuals can average one to three (adults) and three to six (children) ILI yearly [22]. Table 2 reports data collected by Public Health England (PHE) [23] of the average rate per 100,000 practice population of ILI cases observed from October 2013 until March 2014 in each London borough. The data from PHE were obtained by a large surveillance system that monitors in hours general practitioners (GPs) consultations for a number of key clinical indicators. In United Kingdom individuals register with a single primary care physician who has a well defined patient population. On a daily basis this system reports and covers over 40% of the England population. Data were collected from available practices located in each London borough from October 2013 to March 2014, Table 2 reports their average value during these six months.
Table 2

In the second column rate per 100,000 practice population of observed number of ILI cases from October 2013 until March 2014 for each London borough (n=32) are shown

Rate of observed cases

No Underground

No Underground

No Underground

Hammersmith and Fulham

Kensington and Chelsea

Kingston upon Thames

No Underground

No Underground

Richmond upon Thames

No Underground

Tower Hamlets

Waltham Forest

In the third column, each borough Φ-values are presented. A correlation coefficient of 0.44 is obtained

In order to investigate the correlation between ILI rates in London and the contacts arising when using the underground, we define two additional parameters: (i) the total number of contacts occurring for a single passenger during their whole trip Ψ, (ii) and the total number of contacts occurring for all passengers departing from the same borough in the same time interval during the duration their trips Φ.

The quantity Ψ, defined as the sum of all the people each passenger get in contact with during their trip, is \(\Psi = \sum _{i=1}^{n} {\delta }_{i}\) where n is the number of stations the passenger need to cross during the trip and δi is the number of contact the passenger makes in the i-th station and by our model is equal to
$$ \delta_{i} = \langle \rho_{i} \rangle \pi R^{2} $$

where 〈ρi〉 is the average density in the i-th station.

While comparing data and results we need to keep in mind that our model is applied to the very early stages of contagion in environments considerably smaller than the usual scale (a whole city, or even a nation). To be able to perfectly compare the results from our microscopic analysis we would need individual-level data that are very difficult to acquire, thus in the comparison with PHE data (that are population-level data) we need to take into account the inferential fallacy that may occur when statistical properties observed on an aggregate level do not reflect the relations that exist on a local level. In the attempt of overcoming this problem we study the amount of contacts obtained during the whole trips when leaving from stations belonging to the same boroughs. This translate by saying that for each borough we calculate the number Φ that is given by summing for all the trips departing in each underground station of the borough between 5 am and 10 am, the total amount of contacts acquired during each whole trip. Normalised by the total number of people entering the stations of the borough (thus, consequently, the number of departures) between 5 am and 10 am (Nentry).
$$ \Phi = \cfrac{\sum_{n} \Delta_{n} + \sum_{m} \Delta_{m} + \sum_{k} \Delta_{k} + \dots}{N_{entry}} $$

where n is the total number of trips departing from the first station, m is the total amount of trips departing from the second station and so on.

We compared number of contacts calculated using our model with data collected by Public Health England (PHE) of Influenza-like illnesses (ILI) [23] and several interesting results can be highlighted.

First of all, boroughs that do no contain any underground station seem to have incidence rates lower than than average 9.73 (per 100,000). The average ILI incidence in boroughs without underground is 7.61, while it is 10.24 in boroughs with underground station. One exception is Lewisham (11.75) that, however, has a high number of railway stations (London Overground and Docklands Light Railway, DLR), here the 2011 Census [24] reported railway as the principal form of transport that residents of the borough used to travel to work. This difference, however, is not statistically significant (p-value = 0.0776).

We also notice that boroughs with higher case rates are generally more peripheral in respect to others, in particular their underground stations have a more peripheral position on the map, meaning that people who travel from there are forced not only to spend more time on the train, but also to change line one or even several times, consequently getting in contact with a higher number of individuals.

As an example we compared trips from two different boroughs: Islington and the Royal Borough of Kensington and Chelsea (RBKC). Both are very central with respect to the London map, but Islington has a case rate of 15.37 and its stations are more peripheral with respect to the underground map (the majority of the stations are served by only one line), while RBKC has a rate of 5.5 and its stations are more central and well connected and generally served by two or more lines. In order to apply our method in relation to the data, we must consider only the trips made by individuals residing in these boroughs. TfL does not provide personal data of the passengers, however not all underground users reside in London (e.g. tourists, commuters etc), moreover passengers can change position multiple times throughout the day. For this reason, we filtered our data by only considering trips with a departure time between 6 am and 10 am assuming that people leaving for work early in the morning tend to use the more convenient public transport available to them by taking the train from the station closest to their residence. After that, we evaluated all the trips leaving from the stations in these boroughs between the selected time interval, and considered the arrival stations, the lines and stations that were involved, and the total journey time. The first noticeable result is that almost all the trips departing from stations in the borough of Islington have to change line in King’s Cross St. Pancreas station which is one of the busiest and most central station of the entire underground, also connected with a major London railway terminal of the same name. Consequently, for each trip we also need to consider the time the individuals take in this station when changing from a platform to another and the number of contacts they make during the process. The majority of trips departing from RBKC are instead direct trips and do not stop at any intermediate station.

Now, let us evaluate the total number of contacts for several frequent trips departing from stations from both boroughs. Results can be seen in Fig. 2. For trips departing from RBKC most of the time only two stations are involved and therefore
$$ \Delta_{RBKC} = \delta_{O} + \delta_{D} $$
Fig. 2
Fig. 2

Total number of contacts during trips departing from Islington (red) and during trips departing from RBKC (green). We plotted results for the n=3 most common routes per borough. Passengers leaving from Islington, while travelling on the underground, need to change lines more frequently respect to people departing from RBKC thus traversing more stations and getting in contact with more people

where δO is the number of contacts obtained by crossing the origin station, and δD is the number of contact resulted by crossing the destination station.

On the other hand, the majority of trips departing from Islington have to stop in an intermediate station in order to change line, most of the times King’s Cross station. Thus, the total number of contact for each trip is calculated by
$$ \Delta_{RBKC} = \delta_{O} + \delta_{KC} + \delta_{D} $$

where δKC is the number of contacts made at King’s Cross.

This supports our assumption that individuals leaving from the borough with the highest incidence rate make more contacts with respect of the ones leaving from the borough with the lowest incidence rate.

In order to extend our analysis to the whole city, we use the previously defined parameter Φ. The quantity Φ has no direct epidemiological meaning, it is just an index of comparison between our model results and PHE data. For this reason, we do not expect to find a perfect correlation between the data and these values, also because PHE data are very aggregate and do not report personal information relating the infective individuals such as sex or age, and we know, for example that everyday commuting across the city is mostly done by adult individuals. Moreover, contact rates would only be able to capture what happens in the underground while PHE data report infection cases independently from the origin of the contagion (may it be in schools, offices, households etc). However, as can be seen from Φ values reported in Table 2, this very basic type of comparison can still capture a correlation of 0.44. The model can especially capture what happens in boroughs that have generally higher incidence rates and the respective total amount of contact Φ is higher (ex. Greenwich, Newham, Southwark). On the other hand, it is less able to capture what happens in some low incidence boroughs. One explanation is possibly related to the fact that, according to 2011 Census, people living in those boroughs use mostly private means to go to work (car, walk etc) compared to the underground, thus meaning that even though taking the underground from those boroughs involve an average or high amount of contacts, there are actually not many people taking it in the first place (ex. Hounslow, Richmond upon Thames, Merton). Moreover, since the mean incidence rate is 9.73, we divide boroughs into two groups: high incidence rates (≥10) and low incidence rates (<10), and compare the respective Φ-values. Results can be seen in Fig. 3. The model is able to capture the fact that the totality of higher incidence stations have an higher amount of contacts and, vice versa, the totality of lower incidence stations have a lower amount of contacts. A Mann-Whitney U-test was run with the null hypothesis of both sets having an equal median and the test rejected the hypothesis with a p-value of 0.0293; showing a correlation between the total amount of contacts passengers make when using the underground and infection rates.
Fig. 3
Fig. 3

Box Plot Left: Boroughs divided by high (n=15) and low (n=11) incidence rates and their associated Φ-values. It is possible to notice a clear difference between the two sets medians (bands inside the boxes). Moreover, high rates boroughs exhibit a considerably higher interquartile range, meaning that cumulative contact rates for boroughs with ILI incidence rates higher than 10 tend to exhibit a significantly higher range variability respect to boroughs with lower ILI incidence rates. This distance, which represents the middle 50% of data, touches considerably higher Φ-values for high ILI-rates boroughs respect to the values covered by low ILI-rates boroughs. A Mann-Whitney U-test was run with MATLAB with the null hypothesis of both sets having an equal median and the test rejected the hypothesis with a p-value of 0.0293.Right: Boroughs divided by high (n=15) and low (n=11) incidence rates and their associated children demographic rates. It is possible to notice that the difference between the two sets medians (bands inside the boxes) is almost non existent. Moreover, even if high rates boroughs exhibit higher interquartile range, this distance, which represents the middle 50% of data, touches lower children demographic rates for high ILI-rates boroughs respect to the values covered by low ILI-rates boroughs

This correlation is especially significant if we compare our results with with demographic rates from each borough in Table 3, where we can see that the role of public transport in relation to the spread of ILI could be compared to the role of other important key factors. We collected demographic data by borough from London Datastore [25], ranked them according to their ILI rate and aggregated by comparing boroughs according to the mean ILI incidence of 9.73 (as done previously) and studied the correlation. Some of the results reported in the table confirm a statistical significance for some well-known factors such as boroughs inner densities (p=0.0151), employment rates (p=0.0433), claimants of benefits on housing and income support (p=0.0031) and population aged 65+ (p=0.0012). More surprisingly is the low correlation with the younger population aged <15 years old. Many studies have shown the important role played by children in the spread of infectious diseases because of their higher mixing rates and incidence rates and shown that schools constitute some of the main sites of contagion [2633]. When comparing PHE data with demographic data on the portion of the population aged 0–15 years old in each borough (knowing that in UK children generally attend the school in their neighbourhood) we find a correlation coefficient of only 0.13. This result seems, at first, particularly surprising, when thinking about the significant role children play in the spread of ILI. Mughini et al. [34] performed a survey analysis on ILI in families with children younger than 4 years, results showed that parents of ILI-affected children had a concurrent 4-fold higher ILI risk. The small statistical difference in children population between boroughs with low and high ILI rates (Fig. 3), however, does not mean that children do not contribute to the spread of ILI on a population level, on the contrary many factors are involved and the larger the scale of the analysis and grater is the amount of time passed since contagion, more complicated is the understanding of what the cause was. The scope of this analysis is simply to compare some factors whose role in the spread of infections is already acknowledged and discuss the possibility that the use of public transport may play a similar role and should be taken into account along them.
Table 3

Correlation coefficients between the rates of observed ILI cases and some 2015 demographic data for each borough from London Datastore [25]

Correlation coefficients

Underground related contacts

Population size

Inner densities

Employment rates

Employment with degree

Benefits claimants

Cars per households

Population aged 0–15 years old

Population aged 65+

Since the mean incidence rate is 9.73, we divided boroughs into two groups: high incidence rates (≥10, n=15)) and low incidence rates (<10, n=11) and demographic data were divided accordingly. Mann-Whitney U-test was run for all of them with the null hypothesis of both sets having an equal median. It can be seen that, in relation to the spread of infectious diseases, the use of public transport can possibly play a role comparable to the one played by some key factors such as inner densities and employment rates and population by age

The correlation between the use of public transport and the spread of infectious diseases is something that has always been assumed and generally accepted but has never been proved.

Previous studies have highlighted the importance of analysing social contacts and mixing patterns when studying infectious diseases transmitted by the respiratory or close-contact routes. In [26] data were collected by a population-based survey of mixing patterns in eight European countries through a paper-diary methodology. The definition of contact used in the study was of interactions such as a kiss or a handshake for physical contacts, while nonphysical contacts were situations such as a two-way conversation without skin-to-skin contact. In the model that we use [2], instead, we define two individuals as in contact if the local density around them is high enough that they enter in the respective infective regions that surrounds them. Another type of survey based study [35] collected data of ILI cases across UK population through Flusurvey, an internet-based open community cohort. They calculated ILI incidence week by week throughout five months, and investigated possible risk factors associated with it. One of the main conclusions of this study was that public transport does not increase the possibility of acquiring ILI. However, that study took the whole UK into account and the survey approach implied that people were able to report their symptoms at any point throughout their convalescence, moreover symptoms usually arise awhile (even days) after the contagion happens, meaning that people using public transport and people not using it have enough time to mix with each other in other environments (offices, households, etc). Consequently it is not possible to pinpoint exactly where the contagion took place, giving the macroscopic result of no connection between public transport use and incidence of new infection, while a more detailed microscopic analysis would be needed. In our work, instead of taking the whole of UK into account, we focus the attention on a local description where the underground stations are seen as confined and crowded spaces. We study the infection processes on the very first moments of the contagion, actually limiting our study to 15 min segments of intervals, knowing that the mixing patterns that arise once outside the underground network (households, offices etc) will lead to new infections that can blur the public transportation role when looking at the bigger picture. Also, studying the connection between public transport and the spread of airborne infections can highlight another issue i.e. the spatial incidence patterns over time, meaning that it could be possible to find out whether London Underground contribute to how quickly and how strongly a disease spreads to different areas of the city, areas that maybe otherwise would be reached by the disease more slowly or with lower case incidence.

The hypothesis of an association between public transport and disease transmission is not novel. Another study [36] focused on the relationship between public transport use and acquisition of acute respiratory infection (ARI). That study is closer to our work because it uses a more microscopical analysis, both in time and space. Data of ARI and control of other non respiratory conditions were collected from General Practitioners, together with data on bus or tram usage in the five days preceding illness onset (cases) or the five days before consultation (controls) and results show statistically significant association between ARI and bus or tram use. Moreover, other studies have also highlighted the importance of studying metropolitan patterns and social interactions. In [37], using travel smart card data, authors construct a time-resolved in-vehicle social encounter network on public buses in a city and draw attention to the impact of collective regularities can make on various diffusion/spreading processes. Our work, while addressing a similar question, is differentiate by the use of a novel analytical method combining individual-level pedestrian modelling and compartmental modelling. Moreover the two studies [36, 37] analyse transmission in a static setting (buses) while we address the problem in a dynamic environment of people moving inside underground stations.

The results of our study shows the existence of a correlation between the use of public transport and infectious diseases transmission. Specifically, we showed a correlation between the use of London underground and the spread of influenza-like illnesses. The model is particularly able to show this correlation in environments with high numbers of infections, capturing the fact that areas that have the highest numbers of ILI cases are also areas whose inhabitants spend more time in the underground network by changing line more frequently and getting in contact with more individuals. Correlation, however, does not prove causation, when looking at an epidemic on a large scale many contributing factors need to be taken into account. The role of social inequalities and age in the transmission of infectious diseases is well known and publicly accepted [3841]. We compared the correlation between ILI cases and contacts originated from the use of the underground with the correlation coefficients between ILI cases and some demographic factors such as children and elderly populations, boroughs inner densities, employment rates and population on low income support (benefit claimants) and our results show similar values. While these results are not enough to quantify the role played by the use of public transport on large-scale infection transmission, they are interesting for both researchers and policy makers alike.

Limitations of this work are associated to the nature of the datasets involved. On the modelling side, Oyster card data and train tracking data were sourced in different years (2009 and 2013 respectively) and, while the underground network did not undergo major transformations during those years, results could still be affected by a level on uncertainty on the timetables. Also, PHE dataset reports numbers of ILI cases disaggregated only by borough and limited to one season, highly limiting the model’s precision in the evaluation of the correlation between infections and numbers of contacts. Moreover, knowledge of specific ILI-related data such as lengths of incubation periods or different level of infectiousness provided by the different pathogens causing ILI, would make results more precise and case specific. Further studies should focus, in particular, on sourcing individual-level ILI data in different settings so that, by combining them with already existing studies from household and schools, a clearer map of the transmission of ILI in a metropolitan environment could be drawn, this could help quantifying the role that different hot-spots play in the transmission. Empirical studies combining aero-biology and pedestrian modelling would be important in highly improving models fidelity and devising non-pharmaceutical control strategies tackling threshold densities to minimise numbers of infections and optimal ventilation in different crowded environments.

Policy makers, in particular, should address the role potentially played by public transport and crowded events and avoid encouraging the attendance of such environments during epidemics in order to maintain public morale, as specifically done by the UK Influenza Pandemic Preparedness Strategy [1].

In summary, we have analysed the association between the use of public transport and infectious diseases transmission by studying the London Underground network. We used real trips data to infer the level of density in each station at any time during the day and the number of contacts between passengers, and compared these results to influenza-like illnesses (ILI) rates in London boroughs. Results show a correlation between the use of the underground and ILI cases in London, specifically they show that higher numbers of ILI cases arise in those boroughs where the population spend more time in the Underground and/or incur in a higher number of contacts when travelling. On the other hand, lower numbers of ILI cases arise in those boroughs where people have a limited use of the underground and/or incur in fewer contacts. These results are in line with other environmental and demographic factors such as population stratified by ages, inner densities employment and income.

These results are informative for both scientists and policy makers alike. At the basic research stage, further studies are required to explicitly quantify the role of public transport in infectious disease transmission, and policies should be re-evaluated to take these results into account.

We must acknowledge Dr Richard Milton at CASA (UCL) for sharing his train position data in the London Underground; Transport for London (TfL) for sharing their oyster card data; Public Health England (PHE) for their data on influenza-like illness rates in London boroughs and London Datastore for their demographic data on London population.

Not applicable.

Availability of data and materials

Train positions data available from CASA (UCL) [21], Oyster card data available from TfL Open Data Users [17], influenza-like illness rates and London demographic data available from Punlic Health England and London Datastore [23].

Authors’ contributions

LG and AJ conceived, designed the study and coded the model. LG conducted the main analysis and drafted the manuscript. All authors revised the draft manuscript and approved the final version.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

University College London, London, UK
University of Bristol, Bristol, UK

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Sounds of the Mushroom Kingdom

Beneath the Earth, under a Subterranean Sun, is a realm of ambulatory toadstools, scheming and machinating and living their cute little toadstool lives.

Sometimes, they explore the surface.

This book gives all the details of these underworld interlopers in order to enrich your not-in-a-Mushroom-Kingdom campaign.

Sounds of the Mushroom Kingdom: "Putting the Fun in Fungi!”

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Finding the Perfect Fit Through Social Media

Healthcare System Improves Quality of Job Applicants and Hiring Event Numbers with Social Media

Each year, more than 100,000 people apply for jobs with TriHealth, a healthcare system with more than 100 locations, hundreds of services, and a broad range of employees. However, sifting through hundreds of applications looking for qualified applicants was a time consuming task for hiring managers.

2060 Digital built a strong social media presence supported by targeted display advertising campaigns to present job opportunities to quality candidates, while highlighting the organization’s commitment to the community.

TriHealth’s dedicated Social Media Strategist created a Facebook career page with a highly visible Apply Now app. The strategist writes, designs and publishes content highlighting TriHealth’s culture as well as current employee news and contributions from members of the team. In addition, the Social Media Strategist and Digital Project Manager work together to run targeted social and display ads for job openings that reach highly qualified potential employees. One successful collaboration to advertise a nursing recruitment event resulted in 849,780 impressions and 1,687 clicks, and more importantly, 263 sign-ins at the event.

2060 Digital’s Social Media Strategist and Digital Project Manager act as an extension of the TriHealth recruitment team to help attract quality job seekers. The TriHealth team couldn’t be happier.

“Exceptional. That’s the word we would use to describe 2060 Digital’s management (…) and our results. We are extremely pleased with their creativity, their excellent customer service, and the comprehensive reporting we receive,” says the client. “The choice to work with 2060 Digital has been validated over and over. We couldn’t be more satisfied with this partnership. They’re a great addition to our recruitment team!”

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Highway 395 Crash Kills 57-year-old Hesperia Woman

Highway 395 Crash Kills 57-year-old Hesperia Woman

HESPERIA – A 57-year-old Hesperia woman lost her life as a result of a CA-395 crash on Wednesday afternoon.

At around 12:54 p.m. deputies from the Hesperia Station and emergency personnel responded to the collision south of Main Street on CA-395. When they arrived they located to mangled sedans that collided head-on in the southbound lane.  Marylou Hernandez was found unresponsive in a 1988 Honda Accord and was confirmed deceased at 1:10 p.m.

MaryLou, a Shell Gas Station employee was heading northbound on her way to work on the corner of CA-395 and Bear Valley Road.
The second vehicle was driving southbound at the time of the collision. The driver of the second sedan, a male was transported to the hospital for the treatment of non-life-threatening injuries authorities said.

The cause of the collision remains under investigation by the Sheriff’s Major Accident Investigation Team.

For late-breaking news, follow 24/7 Headline News on our Los Angeles County Facebook group, Riverside County Facebook group, Coachella Valley News groupUS News group, Victor Valley/Inland Empire Facebook group. If you like what we are doing and want regular updates on your facebook stream like our Facebook Fan Page. You may also follow 24/7 Headline News on Twitter and Instagram!

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Cedar Creek - Norway Hewn Log Railing Systems

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Interior or exterior Cedar Creek's line of Norway Hewn Log Railings will add functional beauty to your home. Our custom designed handrails are crafted from Northern White Cedar for its light natural appearance and natural decay resistance advantages, and are available in a variety of classic to contemporary styles. All of our railing styles are sturdy and reliable. Our custom-designed handrails are offered in hewn or sanded hewn textured touchable finish. All railings are available with an interior clear finish or choose from our selection of exterior grade finishes.

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Police Nab School Burglary Suspects By Tracking Stolen Electronics

This is an archived article and the information in the article may be outdated. Please look at the time stamp on the story to see when it was last updated.

Police say they caught up to a boy and man suspected of burglarizing a Fayetteville elementary school by using the tracking equipment of the electronics the men stole from the school.

Another suspect was arrested Thursday in the burglary case where police say three suspects broke into a Fayetteville elementary school and stole $13,000 worth of electronics.

Jdonta Britt, 18, was arrested on suspicion of commercial burglary and theft of property. He remains in the Washington County Detention Center on $4,820 bond, according to the Washington County Sheriff’s Office.

An investigation led police to Britt after another suspect in the alleged burglary told police Britt took part in the crime. That suspect, a juvenile male, was arrested Jan. 10 on suspicion of the burglary. The teenager also implicated a third suspect.

Police said the suspects broke into Butterfield Trail Elementary School on Christmas last year and stole 10 Apple iTouches, 12 Apple iPads, a Dell laptop computer, a Dell desktop computer, two digital cameras and other electronic equipment.

Investigators caught the juvenile suspect by following the devices’ tracking software, according to an arrest warrant affidavit from the Washington County Circuit Clerk’s Office.

Police also collected video surveillance of the burglary, police said.


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Top 2 Best Wall Ovens Reviews 2018

Do you love cooking but lack enough space in your apartment kitchen to include a multifunctional oven? There is no need to fret as wall ovens can offer you both flexibility and functionality without demanding much space.

Home chefs who treat cooking no less than an art prefer purchasing wall oven as they keep the food at eye level and make it loads easier to keep a tab on what is being cooked. With numerous varieties of wall ovens available in different price ranges, it can become hard to pick one which has an upper hand over the other models in the same price range.

Wall ovens are available these days with cool features such as incredible controls, blower convention, double convection and a lot more. If you are not sure of the kind of oven that you need to purchase, then you can go through this review of the best walls oven which lists the best performers that are loaded with incredible features and those that are easiest to use and clean.

List of Best Wall Ovens 2018 (Table):-

Best Wall OvensEditor RankingReviewPrice
GE JK3800SHSS 27”#1Read ReviewCheck Price
Empava 24” KQP65B-14-220V#2Read ReviewCheck Price

What is a Wall Oven?

Wall oven are wonderful alternatives in kitchens with space constraints. These make it easier to remove or insert heavy food items as they are positioned quite conveniently.A wall oven expands the cooking options for home chefs. One need not bend down for reaching the oven often when in use. Installing a wall oven at convenient height ensures convenience and flexibility to the functions of the kitchen.

The kind of power supply that you use determines whether you need to opt for an electric or gas wall oven. Electric wall ovens cook food in an even manner when compared to gas oven and are considerably easier to clean. A larger selection of electric units is available in comparison to the gas variants.

Why do you need a wall oven?

The main advantage of having a wall oven is that it imparts flexibility to your cooking activities. As the oven is basically fitted at the eye level, you will never have to bend over for crafting your favorite soufflés. This equipment helps in preventing kitchen crowding.

  • Multiple features

You can have more than one oven while installing a wall oven. This implies that you can make the most out of the wall space that you are using for installing a wall oven. Wall ovens are loaded with incredible functions which can cut down the efforts that you need to put while crafting a delicacy.

  • Optimizing your kitchen space

Purchasing a unit which has both stove and the oven integrated saves a lot of space which can be utilized for placing kitchen table. You do not have to run around the kitchen for crafting your favourite delicacies.

Factors to consider when you buy a wall oven

There are several factors that one need to consider which purchasing a wall oven. The first and foremost thing to consider while purchasing a wall oven is the budget. You need to be clear about your budget and the space that you can allocate for incorporating the wall oven in your kitchen. You need to be clear about the other appliances that you would be using along with your wall oven. Try to identify the features that you are looking for in a wall oven.

  • Cost

Basic wall ovens cost $700. For a double wall oven, you can expect to pay $3500. You will have to pay additional charges based on the functions that you are seeking. For instance, you will have to pay a little more than the basic model if you wish to purchase a self-cleaning wall oven.

  • Size

Wall ovens are available in standard sizes. These are available in widths of 24, 27 and 30 inches. The models vary in size by an inch or two. The standard differs from one brand to another.

  • Single vs. Double

As a wall oven is smaller in size in comparison to a range oven, you can consider having a double wall oven for overcoming the limitation. Those who lack budget and space to accommodate a double wall oven can consider opting for models that offer maximum interior space.

  • Manual or self-clean

You need to question yourself whether it is worth paying additional money for cleaning your wall oven. The decision should be driven based on the frequency of your oven usage.

  • Settings and controls

Most of the wall ovens available in the market today feature electronic displays. This does not imply that all are the same. Wall ovens offer “warm and hold”, “delayed bake”, “variable broil” and a lot more settings to the users. You will have to spend anywhere from $900 to $1,200 if you wish to avail these wall oven features.

What types and designs to consider?

Here are certain aspects that you need to consider before purchasing a wall oven for your kitchen.

  • Gas or electric wall oven?

The kind of power that you utilize in your home determines whether you need to opt for an electric or gas wall oven. If you have no apprehension about going either way, then your final decision should be driven based on your cooking preferences and your budget.

Budget wall and gas ovens are the least expensive models which are offered in the price range of $700 to $1,000. These have either electronic touch pads or dials for controlling the cooking temperatures and the oven settings.

Standard wall ovens comprise of self-cleaning features. This adds up to $200 over the price tag of budget wall ovens. Most of these have electronic touch pads for regulating the temperature and other aspects. Single ovens with microwave comprise of a built-in microwave and a built-in oven. However, these come with a hefty price tag.

Standard convection wall ovens employ fans for uniform distribution of heat. This is responsible for speeding up the roasting and baking times. These cost $300 more than the standard electric ovens.

  • Size of the unit

Wall ovens are preferred by many home chefs as these impart flexibility while organizing the kitchen. These can be installed at a convenient height and makes the entire process of cooking fun and convenient for people who are not fond of stooping while using kitchen appliances. Wall ovens can be purchased based on their width. Before purchasing any model, make sure to check that the oven is offering you the interior space that you seek.

  • Functions, features, and additional offerings

Features in wall ovens boost the convenience and impart value to the purchase made. For instance, the control lockout feature prevents impatient chefs from playing with the oven. Hardworking kitchen chefs will find double ovens pretty useful. These permit you to roast and bake simultaneously at different temperatures.

Most of the wall ovens are equipped with electronic controls except the budget models. These help in setting the precise temperature requirements. The self-cleaning option reduces the burden of a chore. Removable oven doors permit easy and quick wiping and cleaning.

Top 2 Best Wall Oven Reviews 2018

With numerous options available pertaining to wall ovens, it can be hard for people to make the right move while purchasing one. Here are the top 2 Best Wall Ovens for the year that you can consider if you are planning to own the best model available.

1.  GE JK3800SHSS 27” Wall Oven

GE JK3800SHSS Stainless Steel Electric Combination Wall Oven

Check Price

This 27” electric wall oven comprises of a built-in microwave. This model has been crafted in stainless steel with glass touch electronic controls that help in adjusting the oven easily and quickly. 3 self-clean heavy-duty oven racks are featured in the lower oven and these can be configured in six positions.

GE is renowned for integrating the latest technology coupled with exceptional quality in its offerings. This holds true for this wall oven model as well. The timeless appearance of this model makes it an excellent addition to any family kitchen. The capacity of 7.7 cubic feet ensures that you can cook several dishes at a time. The microwave offers 1000W cooking power with 10 cooking levels for defrosting, reheating and boiling. Sensor cooking controls comprise of a built-in sensor that automatically adjusts the power levels and cooking times.

The turntable rotates the food throughout the cooking cycle to ensure even cooking. The users can make use of the Delay Start feature that permits them to program the oven at a time predetermined by them. Full broil coverage is offered by the eight-pass broil element.

The control lock safety feature ensures that the oven is not used unintentionally. The self-cleaning feature removes the hassles associated with cleaning ovens. The big clear oven window without any frame ensures that you can keep a tab on the food being cooked without having to open the door. A limited warranty is offered over this appliance for a period of 1 year from the date of purchase.

  • Big clear view window for tracking the cooking progress
  • Built-in sensors for monitoring and adjusting the temperature
  • Steam cleaning feature that is safer than cleaning with chemicals
  • Glass Touch Controls
  • Multiple position racks
  • Accurate cooking temperature and options

  • Uneven heating
  • Complicated controls
  • No exterior ventilation design thereby allowing the heat to vent into the kitchen
  • Black glass doors show every fingerprint and smudge thereby demanding constant cleaning

2.  Empava 24” KQP65B-14-220V Wall Oven

Empava KQP65B-14-220V Tempered Glass Electric Built-in Single Wall Oven

Check Price

This oven offered by Empava stands out in terms of delivering premium functionality at a price tag that can appease those with a lower budget. This chef series electric wall oven offered by Empava is loaded with all essential features and also promises durability. This stylishly designed model competes with all the premium brands wall ovens available in the market for a fraction of theprice.

This single wall convection oven comprises of 2 layered tempered glass. It has been fabricated with stainless steel and comprises of six different settings for cooking. The model also features a forced cooling exhaust system. The attractive modern design and amazing features will make this model by Empava your favourite as it is one of the most visually appealing wall ovens you can find in the market today.

If we take a look at the features, this wall oven is ideal for use in all spaces including homes, small kitchens, restaurants, and apartments. This wall oven comprises an aluminum casing and an inner shell made of enamel. This makes it quite easy to clean this oven. The oven comprises of oversized oven window with two layers of tempered glass which ensures that you can easily monitor the progress of your cooking.

The safety technology forced cooking in addition to the exhaust system cools and prevents any accidents. This product has been engineered as well as designed in the USA. The product has been designed by adhering to stringent safety standards. Six functions are offered to the users which ensure convenience during the cooking process: Bake, convection roast, roast, true convection broil, broil and preheat.

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text:
Family Business Succession: The Final Test of Greatness

By Craig E Aronoff, Stephen L Mc Clure, John L Professor Ward
Helps to prepare for passing the family business on to the next generation. Leaders will learn how to create a succession plan; how to develop opportunities for succession candidates; how to build consensus with the family and leaders on succession plans and finally leaders will learn when and how to let go of their own role in the business.

  Choose a format

Price: $23.00/ea

1 $23.00 9780230111004 No volume discount available.

About the Paperback

Publisher Palgrave MacMillan
Publish date 01/04/2011
Pages 102
ISBN-13 9780230111004
ISBN-10 0230111009
Language English


text:
For those victimized by trafficking, sometimes the overarching emotion is not fear or hurt, but loneliness. Some girls even go back to “the life” because of it. Some desperately drag friends into the life with them simply because of the innate need for companionship. Have you ever experienced loneliness? True loneliness?

It’s not a “woe is me” mentality. We’re not talking Eeyore here. It’s more of a feeling of being rejected. It’s an intense ache of your soul that’s similar to the grief a person feels after losing a loved one. Your heart tells you that you should be in community with another, but that individual simply isn’t there. You feel so isolated, forgotten, misunderstood and unworthy. A lonely person feels diseased.

Mother Teresa, right, talks with patients coming to the mobile leprosy clinic at Dhapa outside Calcutta, India, in this 1960's handout photo made available 10/10/03. (AP Photo/Missionaries of Charity, HO - NO SALES Original Filename: ITALY_MOTHER_TERESA_ROM117.jpg)

Have you ever thought of what it must have been like for a leper in Biblical times, or for the lepers in modern day Indian leper colonies? I have. You see, leprosy is a chronic disease that’s caused by bacteria. In short, it affects the skin, nerves and eyes of the infected person. One source about the topic said, “For many years, [leprosy] was considered [mysterious and] associated with some type of curse, and persons with the disease were isolated and ostracized.” This statement sounds a lot like our culture’s perception of prostitution today… mysterious, associated with a curse – like being a bad person, and those “diseased” are ostracized. Pimps give their victims a disease called Trauma, by means of injection, through prostitution.

This also reminds me of the HIV/AIDS struggle in our country. HIV positive can probably speak to similar experiences of the judgmental looks, women taking a step back in an elevator or men wiping their hands off on their pants after coming in contact with you. No more can we equate Leprosy, or HIV, or the aftermath of forced prostitution with a person being “bad.” Victims of disease are just that—victims. They are common people affected by a bad disease, not bad people affected by a common disease.

I had the honor of hearing another survivor’s story recently and she too commented on this aspect of loneliness. Like her, my desire to eradicate loneliness is strong. Because of this, I’m sending out a challenge… specifically to churches, faith groups and healthy families: invite in the people with broken pieces! Please do NOT try to go out and build a “safe house” on your own, try to “rescue” girls off the street and into your homes in one night — unless you’re a professional. What I ask instead is that you invite those with broken hearts into your own heart for healing. Churches/faith groups – contact your local professionals (social service providers, law enforcement, etc) to see how you can support their efforts! Families – keep your unit strong and then look into mentor, foster, or adoption programs for children in need! (I’ve been so blessed to read comments from all of you adoptive parents out there!)

We ALL can play some part in curing the leprosy of trafficking.

May this blog serve as an education to those who do not yet know or understand the atrocities of trafficking and may it serve as an encouragement to those who understand it all too well.

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Leave a comment

  1. liz

     /  October 25, 2011

    Your story is so unbelievably inspiring, and the fingerprints of God are all over you. Thank you for your spirit and your heart… it is greatly appreciated. Your journey is one that I know the Lord just smiles upon. I know I don’t even know you, but wow… praying for you and those you come in contact with. May you be such a continual light in such a dark place. Thank you for sharing!

  2. I’m in. Loving this!

  3. I heard about your story from Lisa’s blog. Thank you for courageously sharing your story. Looking at my own 8 and 10 year old daughters, my heart broke for you and other girls living in such abusive, desperate situations. It is hard to imagine, but it is real. Thank you.

  4. Nik

     /  October 24, 2011

    I’m game!

  5. I love this challenge!!! And believe in inviting in those with broken hearts, and entering into the pain and loneliness. For those who want to become more involved specifically with opening our hearts and homes to girls who have escaped, do you have any resources of where to start? I know that lonely, broken people surround us, and we must be God’s agents of healing to those we encounter every day, but also just wondering how to get involved more specifically with people in this situation. Thanks so much!

    • GREAT question Lauren! It depends on where you’re located really… there some good organizations out there that I’d be happy to reccomend if you can answer a few questions for me… 1) what part of the country do you live in? 2) are you interested in helping fight international trafficking, domestic trafficking or both (no correct answer here, there are needs everywhere!) and 3) would you like some more educational resources or do you feel you’re ready to get in there and serve?

      FYI – I’ll probably write a post soon on some of these resources. Hopefully that’ll be of some general help to everyone as well.

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text:
Back in February, Google announced that Nest would return after a stint as an independent Alphabet company. The smart home maker joined the hardware division to drive closer integration, with Google today revealing that Nest will be a part of the home and living room team. Rishi Chandra will still lead that group at the expense of Nest CEO Marwan Fawaz.

According to CNET, Fawaz’s departure was something advocated for by Nest employees. The former telecom industry executive and CEO of Motorola Home was seen as “operations manager” rather than an innovative leader.

Fawaz was named CEO by Alphabet’s board after the departure of Tony Fadell in 2016. However, he never found his footing, with some at Nest saying that this tenure only resulted in “on-time mediocre products” rather than more innovative devices.

Meanwhile, over the past five months, employees questioned why Nest retained a general manager figure in Fawaz now that it was under Rick Osterloh. Since the merger, the smart home maker remained under the general umbrella of Google Hardware.

For his part, Osterloh disagrees with the characterization made of Fawaz, a former colleague from Motorola. Fawaz will remain as an executive adviser for the hardware business, Alphabet companies, and other “future strategic investments.”

In contrast, many internally are applauding the appointment of Rishi Chandra, a 12 year veteran that led hardware like Google TV, Chromecast, and currently oversees Google Home. Last seen on stage at the 2017 Made by Google event, Chanda noted in an interview that this merger is a “natural evolution” and that the plan is to build more connected experiences between products.

Nest has had a rocky history since the 2014 acquisition by Google. Some point to the Alphabet reorganization in 2015 as the cause, with Nest given a new progress mandate that came into conflict with one of the original principles of the merger. Namely, that Fadell and company would be allowed to innovate within Google and take advantage of the larger company’s resources.

Meanwhile, according to CNET, Alphabet held talks with Amazon to sell Nest in 2016. The deal obviously did not occur, with Fadell apparently serving as an obstacle by not wanting to take part in the sale.

Update: Google and Rick Osterloh issued a statement this evening rebutting some “unsourced comments” from CNET’s report. It defends Fawaz’s tenure as CEO, noting that “employee retention and satisfaction have significantly improved over his time with the team.”

“We are accelerating the merger of our Home and Nest teams to provide our users and businesses with even better services under the leadership of Rishi Chandra. Marwan Fawaz is a strong CEO with a track record of operational excellence who led the integration planning efforts and was responsible for determining our organizational strategy. Marwan has done a remarkable job driving revenue growth, improving product quality, expanding the product portfolio and user satisfaction at Nest. Nest’s employee retention and satisfaction have significantly improved over his time with the team. Unsourced comments suggesting otherwise are wrong. I am pleased to report that he will stay with us as an executive advisor for our hardware business, for Alphabet companies, and future strategic investments.”

Check out 9to5Google on YouTube for more news:

About the Author


text:
Beeebo-us (98% positive all-time feedback) via Amazon offers the dodocool 6-in-1 USB C Hub w/ HDMI, USB 3.0 ports and charging passthrough for $16.49 Prime shipped when code 8VRQWHFD has been applied at checkout. That’s $12 under our previous mention, is the best we’ve tracked all-time and the lowest out there. Rated 4+ stars from 80% of shoppers.

dodocool 6-in-1 USB C Hub features:

  • Extend three SuperSpeed USB 3.0 ports, one Power Delivery USB-C charging port, one SD memory card slot and one TF memory card slot from one USB-C port or Thunderbolt 3 (USB-C) port.
  • Simultaneously connect up to three USB-A peripherals to a USB-C laptop and transfer data at up to 5 Gbps. Backward compatible with USB 2.0 and USB 1.1 Type-A devices.
  • Charge a Power Delivery enabled USB-C laptop via the USB-C charging port of this product.
  • Enable you to easily access files from an SD card and/or a TF card on your USB-C laptop, up to 104MB/s. Support up to 512GB SD memory cards and up to 128GB TF memory cards. (Memory cards are not included.)
  • Thin, lightweight and compact. No driver required. Plug and play. Support hot swapping function.

About the Author

Blair Altland's favorite gear


text:
C130 training exercise highlights 440th Airlift Wing's importance

Budget documents just released call for retiring the entire 440th Air Wing fleet of C-130s.
March 10, 2014 4:07:00 PM PDT
he Pentagon is making the case for Congress to sign off on sweeping new budget cuts aimed at creating a leaner, more nimble military.

Those cuts would take aim at the C-130s that are a part of the 440th Airlift Wing at Pope Army Airfield at Fort Bragg.  The 440th is on the chopping block.

"It is very much going to affect every single one of our reservists and their families," said Maj. Lisa Ray.

Facing extinction under the newly proposed Pentagon budget, the 440th was eager to show what they do Monday to local media including ABC11.

We watched as they simulated securing a soldier suffering a seizure. In live combat situations, the unit evacuates troops shot on the battlefield, injured by IEDs, or in psychological distress.

"If I get a phone call, or anybody in my unit, 24 hours from that point we have to be at the unit," said Col.  Tom Hansen."Seventy-two hours from that phone call, we're expected to be anywhere in the world performing our mission."

Monday's exercise was a four-hour mission from Fort Bragg to the Outer Banks and back. The training is important because the 440th is Bragg's only unit capable of long distance paratrooper travel.

Of course, many Fort Bragg forces are counted on for quick response to global trouble spots. Critics of the cuts say removing the 440th from post could harm that.

Others worry about the economic impact. The Air Force estimates the wing pumps about $77 million dollars into the Fayetteville economy with its 1,200 reserve and active duty airmen.

"It's making ends meet. It's losing another part of my family," said Capt. MacKenzie Johnson. "So if we have to disband and move from here, it's like losing part of your family."

A plan to replace the 440th's aging fleet of C-130Hs with the more modern C-130Js was put on hold six months ago.

The airmen at Bragg are quietly hoping that plan can be resurrected to save the unit.

See It On TV | Report A Typo |  Send Tip |  Get Alerts | Send us photos
Follow @abc11 on Twitter  |  Become a fan on Facebook


text:
Man charged after deadly crash

September 20, 2009 9:16:12 PM PDT
A Chicago man is facing multiple charges after a deadly traffic accident on the city's Southwest Side.Carlos Chavez, 30, of the 1600 block of W. 21st St. was killed after his vehicle collided with a truck at 2200 S. Cermak on Saturday. Authorities say he was pronounced dead at Stein Institute.

According to Chicago police, the driver of the truck, 35-year-old Roberto M. Torres, was arrested and charged Sunday with DUI, operating a motor vehicle without insurance, failure to stop at a solid red signal, reckless homicide and leaving the scene of accident.

Both Torres and a 20-year-old female passenger were treated for minor injuries at Chicago area hospitals.


text:
Why Do They Want to Choose You?

Living in a very competitive labor market is very hard these days, specially if you are looking for a new job, opportunity or a short term outsourced project to do on a side of your permanent job. It is too hard to get an interview, and it is much harder to be called for a follow up interview or at least to discuss your requirements as an employee with the expected salary amount that you will write down, and then they will offer half of it.

this case is not only for employees, it is also for instructors in many fields (soft skills instructors, Scuba Diving instructors, Yoga, body building, Dancing ….etc) … also for artists, signers, musicians, youtubers, podcasters  every skill has a huge competition these days and it is too damn hard to get between them.

So the question is Why the employer should choose you ?

the Answer is (differ yourself from your competitors) and it comes in stages:

  • Be a professional in your field

The first step is to be a professional in your field, cover every information in the field that you want, reach the level that if anyone asked you any question, you are ready to answer and ready to give extra information, because if you couldn’t answer, then you don’t know ! .. expect any question and be ready for stupid comments on your answer from people who like to ask only, deal with them in a professional way to reflect to the people that you are the man.

but this is not enough, because there is always others who know more than you, or know what you know exactly, but congratulation, you reach the level of being in this group of people who are 20% of the people in your field.

  • Start talking

After having the confident that you know what 80% of people don’t know, start talking about what you know, a man told me one time “I should not be down to earth, i should show that I’m equal to them in this particular field, no I’m better than you, I’m unique, because I can do something I cannot do, always say this to everyone, but only say it if it is about the field you are professional in” … and I believe in this, because every person is a potential employer for you, if this potential employer didn’t see that you are proud of what you have, and you always talk about it, then you will not be any close to the potential employment you will get.

  • Be Up to Date

This is the point that can really make you unique from others, specially from older people who have more experience but more duties to do that they are not free to follow the updated practices about the profession and the field you are in. Let’s assume that you are a Videographer, always follow what new softwares, techniques, and gears are produced, because if you do, now you are a head step of people who are now working on a project and can’t find the time to sleep.

  • Participate in the discussions

Because you will start to be known in the field, start a discussion about the topics that are related to what you know, give your personal opinions and ask for other’s opinions. As a photographer hobbyist, I was able to learn new things from kids, talking about things that I didn’t know from their point of view, and it helped me a lot in my hobby.

  • Create your name

Now you are ready, ready to create a new thing in the field, something that will make your name well known in the field you are in. For example, if you are a professional career trainer, a content that is unique, for a need that is very hard, that will make everyone connect you to that need. Be unique in your designs, the way you give the information, that way you wear your clothes, how you talk, how you listen, the things that you do in daily bases, the things you eat, the way you smile, the way you laugh, and in any thing that is related to the field, and this will make you unique.

Being Unique in your field will get to you more work, and more headache, the positive and the negative. That will force you to know how to deal with bad moments, and bad comments, and will teach you how to change the negative into a positive to work on yourself more, because remember, people don’t like change, and don’t accept the uniqueness from the first time.

Add this to your career path, and put an effort on marketing yourself, and that will make you see the answer of (why they want to choose me?)

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Snow and Ice

Absolute Landscape provides complete snow removal services to our commercial clients. Allow us to keep your business or community accessible during inclement weather by clearing walkways, roads, entrances, etc. Our professionals are available 24 hours a day before as well as during a snow/ice event in order to better serve our clients.

• Snow plowing
• Ice control
• Snow hauling


text:
Perspectives of Innovations, Economics and Business

Volume 10
Paper Author
Sovereign wealth funds in the globalization of financial markets Djordje Cuzovic 1
A study on relevance of demographic factors in investment decisions N. Geetha, M. Ramesh 2
On some aspects of business-state relations in Russia Ignaty Dyakov 3
Energy saving opportunities in Jordanian pharmaceutical industries Mohammad Al-Tahat, Areen Al-Ali 4
Determinants of environmental management systems (EMSs) of manufacturing small and medium scale enterprises (SMEs) in Sri Lanka Sudath Weerasiri, Ravindra Dissanayake, Zhang Zhengang 5

[These Articles are generally selected for this Volume and shall undergo review and editorial procedures until online and print publication. Authors should wait for the final decision of editors. Only when the DOI and full text are assigned - then these Articles' records are final and verified. Planned/Called number of Volumes and Articles may change depending on stream of submission and their quality. So, Authors should keep attention on the update status of Volumes and Articles.]

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May. 12th, 2009 @ 09:44 pm Mystery Car

img_2807, originally uploaded by adameros.

I'm having a devil of a time trying to id this car. The logo on the hood looks like an "AB" or an "A8". Anyone know of a good vintage car logo identification site?

About this Entry
Ceci n'est pas une personne.
Date:May 13th, 2009 05:52 am (UTC)
(Permanent Link)
[User Picture Icon]
Date:May 13th, 2009 05:56 am (UTC)
(Permanent Link)
That helped immensely! It is an Autobianchi Bianchina.
[User Picture Icon]
Date:May 13th, 2009 06:40 am (UTC)
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hmm, looks like an 'little old blue car' to me ;)


text:
Solar Panel Cleaning & Service

Is your solar system being affected by dirty solar panels?

It has been estimated you can lose up to 25% efficiency from having dirty panels.

To produce electricity from the solar you need the sun to get to the panel cells, and if the glass is dirty this doesn’t work very well.

dirty solar panelIs this something you could do yourself?  Maybe, but being on a roof is inherently dangerous, so we can’t recommend it, especially when you add water on to the roof as well.

If your panels are flat on the roof, ie. not at an angle, these need to be checked and cleaned on a regular basis, as dirt and debris build-up will very quickly take it’s toll on the system performance.  We have seen moss growing in the corners of panels when they lie flat, not a good look.

We offer to clean your solar panels, and at the same time we will have a look over your solar system for any signs of wear or degradation.

Call us now on 1300 130 546