scholarly journals Predicting COVID-19 incidence in French hospitals using human contact network analytics

2021 ◽  
Author(s):  
Christian Selinger ◽  
Marc Choisy ◽  
Samuel Alizon
Keyword(s):  
Hospital Admissions ◽  
Human Mobility ◽  
Disease Incidence ◽  
Contact Network ◽  
Mobility Data ◽  
Human Contact ◽  
Network Analytics ◽  
Administrative Units ◽  
Individual Tracking ◽  
National Analysis

Coronavirus disease (COVID-19) was detected in Wuhan, China in 2019 and spread worldwide within few weeks. The COVID-19 epidemic started to gain traction in France in March 2020. Sub-national hospital admissions and deaths were then recorded daily and served as the main policy indicators. Concurrently, mobile phone positioning data have been curated to determine the frequency of users being colocalized within a given distance. Contrarily to individual tracking data, these can provide a proxy of human contact networks between subnational administrative units. Motivated by numerous studies correlating human mobility data and disease incidence, we developed predictive time series models of hospital incidence between July 2020 and April 2021. Adding human contact network analytics such as clustering coefficients, contact network strength, null links or curvature as regressors, we found that predictions can be improved substantially (more than 50%) both at the national and sub-national for up to two weeks. Our sub-national analysis also revealed the importance of spatial structure, as incidence in colocalized administrative units improved predictions. This original application of network analytics from co-localisation data to epidemic spread opens new perspectives for epidemics forecasting and public health.

scholarly journals The effect of mobility restrictions on the SARS-CoV-2 diffusion during the first wave: what are the impacts in Sweden, USA, France and Colombia

2021 ◽  
Author(s):  
Telle Olivier ◽  
Samuel Benkimoun ◽  
Richard Paul
Keyword(s):  
Social Media ◽  
Human Mobility ◽  
Great Majority ◽  
Control Measures ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The Usa ◽  
Administrative Units ◽  
Individual Scale ◽  
The Individual

ResuméCombined with sanitation and social distancing measures, control of human mobility has quickly been targeted as a major leverage to contain the spread of SARS-CoV-2 in a great majority of countries worldwide. The extent to which such measures were successful, however, is uncertain (Gibbs et al. 2020; Kraemer et al. 2020). Very few studies are quantifying the relation between mobility, lockdown strategies and the diffusion of the virus in different countries. Using the anonymised data collected by one of the major social media platforms (Facebook) combined with spatial and temporal Covid-19 data, the objective of this research is to understand how mobility patterns and SARS-CoV-2 diffusion during the first wave are connected in four different countries: the west coast of the USA, Colombia, Sweden and France. Our analyses suggest a relatively modest impact of lockdown on the spread of the virus at the national scale. Despite a varying impact of lockdown on mobility reduction in these countries (83% in France and Colombia, 55% in USA, 10% in Sweden), no country successfully implemented control measures to stem the spread of the virus. As observed in Hubei (Chinazzi et al. 2020), it is likely that the virus had already spread very widely prior to lockdown; the number of affected administrative units in all countries was already very high at the time of lockdown despite the low testing levels. The second conclusion is that the integration of mobility data considerably improved the epidemiological model (as revealed by the QAIC). If inter-individual contact is a fundamental element in the study of the spread of infectious diseases, it is also the case at the level of administrative units. However, this relational dimension is little understood beyond the individual scale mostly due to the lack of mobility data at this scale. Fortunately, these types of data are getting increasingly provided by social media or mobile operators, and they can be used to help administrations to observe changes in movement patterns and/or to better locate where to implement disease control measures such as vaccination (Pollina & Busvine 2020; Pullano et al. 2020; Romm et al. 2020).

scholarly journals The interplay of spatial spread of COVID-19 and human mobility in the urban system of China during the Chinese New Year

2020 ◽  
pp. 239980832095421 ◽  
Author(s):  
Xiaoyan Mu ◽  
Anthony Gar-On Yeh ◽  
Xiaohu Zhang
Keyword(s):  
Human Mobility ◽  
Disease Incidence ◽  
Urban System ◽  
Disease Spread ◽  
New Wave ◽  
Spatial Spread ◽  
Mobility Data ◽  
Rapid Spread ◽  
Disease Spreading ◽  
Scientific Attention

The rapid spread of infectious diseases is devastating to the healthcare systems of all countries. The dynamics of the spatial spread of epidemic have received considerable scientific attention. However, the understanding of the spatial variation of epidemic severity in the urban system is lagging. Using synchronized epidemic data and human mobility data, integrated with other multiple-sourced data, this study examines the interplay between disease spread of coronavirus disease (COVID-19) and inter-city and intra-city mobility among 319 Chinese cities. The results show a disease spreading process consisting of a major transfer (inter-city) diffusion before the Chinese New Year and a subsequent local (intra-city) diffusion after the Chinese New Year in the urban system of China. The variations in disease incidence between cities are mainly driven by inter-city mobility from Wuhan, the epidemic center of COVID-19. Cities that are closer to the epidemic center and with more population in the urban area will face higher risks of disease incidence. Warm and humid weather could help mitigate the spread of COVID-19. The extensive inter-city and intra-city travel interventions in China have reduced approximately 70% and 40% inter-city and intra-city mobility, respectively, and effectively slowed down the spread of the disease by minimizing human to human transmission together with other disease monitoring, control, and preventive measures. These findings could provide valuable insights into understanding the dynamics of disease spread in the urban system and help to respond to another new wave of pandemic in China and other parts of the world.

scholarly journals Exploring the influence of human mobility factors and spread prediction on early COVID-19 in the USA

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhicheng Zheng ◽  
Zhixiang Xie ◽  
Yaochen Qin ◽  
Kun Wang ◽  
Yan Yu ◽  
...  
Keyword(s):  
New York ◽  
Turning Point ◽  
Early Stage ◽  
Human Mobility ◽  
The United States ◽  
Future Trend ◽  
Mobility Data ◽  
Intervention Measures ◽  
The Usa ◽  
Administrative Units

Abstract Background COVID-19 is still spreading rapidly around the world. In this context, how to accurately predict the turning point, duration and final scale of the epidemic in different countries, regions or cities is key to enabling decision makers and public health departments to formulate intervention measures and deploy resources. Methods Based on COVID-19 surveillance data and human mobility data, this study predicts the epidemic trends of national and state regional administrative units in the United States from July 27, 2020, to January 22, 2021, by constructing a SIRD model considering the factors of “lockdown” and “riot”. Results (1) The spread of the epidemic in the USA has the characteristics of geographical proximity. (2) During the lockdown period, there was a strong correlation between the number of COVID-19 infected cases and residents’ activities in recreational areas such as parks. (3) The turning point (the point of time in which active infected cases peak) of the early epidemic in the USA was predicted to occur in September. (4) Among the 10 states experiencing the most severe epidemic, New York, New Jersey, Massachusetts, Texas, Illinois, Pennsylvania and California are all predicted to meet the turning point in a concentrated period from July to September, while the turning point in Georgia is forecast to occur in December. No turning points in Florida and Arizona were foreseen for the forecast period, with the number of infected cases still set to be growing rapidly. Conclusions The model was found accurately to predict the future trend of the epidemic and can be applied to other countries. It is worth noting that in the early stage there is no vaccine or approved pharmaceutical intervention for this disease, making the fight against the pandemic reliant on non-pharmaceutical interventions. Therefore, reducing mobility, focusing on personal protection and increasing social distance remain still the most effective measures to date.

The Seasonal End of Human Coronavirus Hospital Admissions with Implications for SARS-CoV-2 (Preprint)

2020 ◽  
Author(s):  
Alan T Evangelista
Keyword(s):  
Hospital Admissions ◽  
Disease Incidence ◽  
Influenza Viruses ◽  
Seasonal Effects ◽  
Human Coronavirus ◽  
Enveloped Virus ◽  
Multiplex Pcr Assay ◽  
Contact Transmission ◽  
Human Coronaviruses

UNSTRUCTURED The seasonality of influenza viruses and endemic human coronaviruses was tracked over an 8-year period to assess key epidemiologic reduction points in disease incidence for an urban area in the northeast United States. Patients admitted to a pediatric hospital with worsening respiratory symptoms were tested using a multiplex PCR assay from nasopharyngeal swabs. The additive seasonal effects of outdoor temperatures and indoor relative humidity (RH) were evaluated. The 8-year average peak activity of human coronaviruses occurred in the first week of January, when droplet and contact transmission was enabled by the low indoor RH of 20-30%. Previous studies have shown that an increase in RH to 50% has been associated with markedly reduced viability and transmission of influenza virus and animal coronaviruses. As disease incidence was reduced by 50% in early March, to 75% in early April, to greater than 99% at the end of April, a relationship was observed from colder temperatures in January with a low indoor RH to a gradual increase in outdoor temperatures in April with an indoor RH of 45-50%. As a lipid-bound, enveloped virus with similar size characteristics to endemic human coronaviruses, SARS-CoV-2 should be subject to the same dynamics of reduced viability and transmission with increased humidity. In addition to the major role of social distancing, the transition from lower to higher indoor RH with increasing outdoor temperatures could have an additive effect on the decrease in SARS-CoV-2 cases in May. Over the 8-year period of this study, human coronavirus activity was either zero or >99% reduction in the months of June through September, and the implication would be that SARS-Cov-2 may follow a similar pattern. INTERNATIONAL REGISTERED REPORT RR2-doi.org/10.1101/2020.05.15.20103416

scholarly journals Observing the silent world under COVID-19 with a comprehensive impact analysis based on human mobility

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaobin Wang ◽  
Yun Tong ◽  
Yupeng Fan ◽  
Haimeng Liu ◽  
Jun Wu ◽  
...  
Keyword(s):  
Impact Analysis ◽  
Human Mobility ◽  
Economic Activities ◽  
Underdeveloped Countries ◽  
Mobility Data ◽  
Long Run ◽  
Level Of Economic Development ◽  
Human World ◽  
Dynamic Relationships ◽  
The Impact

AbstractSince spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.

scholarly journals Mobility Patterns in Different Age Groups in Japan during the COVID-19 Pandemic: a Small Area Time Series Analysis through March 2021

2021 ◽  
Author(s):  
Shuhei Nomura ◽  
Yuta Tanoue ◽  
Daisuke Yoneoka ◽  
Stuart Gilmour ◽  
Takayuki Kawashima ◽  
...  
Keyword(s):  
Care Delivery ◽  
Human Mobility ◽  
Age Groups ◽  
Virus Transmission ◽  
Initial Response ◽  
Health Care Delivery System ◽  
Population Characteristics ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The Status

AbstractIn the COVID-19 era, movement restrictions are crucial to slow virus transmission and have been implemented in most parts of the world, including Japan. To find new insights on human mobility and movement restrictions encouraged (but not forced) by the emergency declaration in Japan, we analyzed mobility data at 35 major stations and downtown areas in Japan—each defined as an area overlaid by several 125-meter grids—from September 1, 2019 to March 19, 2021. Data on the total number of unique individuals per hour passing through each area were obtained from Yahoo Japan Corporation (i.e., more than 13,500 data points for each area). We examined the temporal trend in the ratio of the rolling seven-day daily average of the total population to a baseline on January 16, 2020, by ten-year age groups in five time frames. We demonstrated that the degree and trend of mobility decline after the declaration of a state of emergency varies across age groups and even at the subregional level. We demonstrated that monitoring dynamic geographic and temporal mobility information stratified by detailed population characteristics can help guide not only exit strategies from an ongoing emergency declaration, but also initial response strategies before the next possible resurgence. Combining such detailed data with data on vaccination coverage and COVID-19 incidence (including the status of the health care delivery system) can help governments and local authorities develop community-specific mobility restriction policies. This could include strengthening incentives to stay home and raising awareness of cognitive errors that weaken people's resolve to refrain from nonessential movement.

scholarly journals Comparative Analysis of Geolocation Information through Mobile-Devices under Different COVID-19 Mobility Restriction Patterns in Spain

2021 ◽  
Vol 10 (2) ◽  
pp. 73
Author(s):  
Raquel Pérez-Arnal ◽  
David Conesa ◽  
Sergio Alvarez-Napagao ◽  
Toyotaro Suzumura ◽  
Martí Català ◽  
...  
Keyword(s):  
Mobile Phones ◽  
Human Mobility ◽  
The Other ◽  
Data Sources ◽  
Experimental Setting ◽  
Mobile Data ◽  
Restriction Patterns ◽  
Mobility Data ◽  
The World ◽  
Proper Interpretation

The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility, being the greatest facilitator for the spread of the virus, is at the epicenter of this change. In order to study mobility under COVID-19, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to future crisis, we need to understand all possible mobility data sources at our disposal. Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is focused on individuals rather than on transportation means. Furthermore, the sample of society they cover is large and representative. On the other hand, these data are not directly accessible for anonymity reasons. Thus, properly interpreting its patterns demands caution. Aware of that, we explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting due to both its large and fast pandemic peak and its implementation of a sustained, generalized lockdown. Our work illustrates how a direct and naive comparison between sources can be misleading, as certain days (e.g., Sundays) exhibit a directly adverse behavior. After understanding their particularities, we find them to be partially correlated and, what is more important, complementary under a proper interpretation. Finally, we confirm that mobile-data can be used to evaluate the efficiency of implemented policies, detect changes in mobility trends, and provide insights into what new normality means in Spain.

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