scholarly journals Potential influence of meteorological conditions on early COVID-19 transmission dynamics in 409 cities across 26 countries

2021 ◽  
Author(s):  
Rachel Lowe ◽  
Ben Armstrong ◽  
Sam Abbott ◽  
Sophie Meakin ◽  
Kathleen O'Reilly ◽  
...  
Keyword(s):  
Early Phase ◽  
Reproduction Number ◽  
Time Window ◽  
Meteorological Factors ◽  
Meteorological Conditions ◽  
Transmission Dynamics ◽  
Potential Influence ◽  
Government Interventions ◽  
Government Response ◽  
Pharmaceutical Interventions

<p>More than a year since its emergence, there is conflicting evidence on the potential influence of weather conditions on COVID-19 transmission dynamics. Respiratory viral infections often show seasonality, with influenza and other coronaviruses peaking in winter, yet the underlying mechanisms are poorly understood. As SARS-CoV-2 is a new virus to humans, it is difficult to ascertain if seasonal climate variations might have enhanced or reduced transmission in the first pandemic wave given the high proportion of susceptible people and the potential confounding role of different types of non-pharmaceutical interventions adopted at different times after the onset of local outbreaks. We used a two-stage ecological modelling approach to estimate weather-dependent signatures in COVID-19 transmission in the early phase of the pandemic, using a dataset of 3 million COVID-19 cases reported until 31 May 2020, spanning 409 locations in 26 countries. We calculated the effective reproduction number (R<sub>e</sub>) over a city-specific early-phase time-window of 10-20 days, for which local transmission had been established but before non-pharmaceutical interventions had intensified, as measured by the OxCGRT Government Response Index. We calculated mean levels of meteorological factors, including temperature and humidity observed in the same time window used to calculate R<sub>e</sub>. Using a multilevel meta-regression approach, we modelled nonlinear effects of meteorological factors, while accounting for government interventions and socio-demographic factors. A weak non-monotonic association between temperature and R<sub>e</sub> was identified, with a decrease of 0.087 (95% CI: 0.025; 0.148) for an increase in temperature between 10-20°C. Non-pharmaceutical interventions had a greater effect on R<sub>e</sub> with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by early government interventions was 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and governmental intervention are more important drivers of transmission.</p>

Analysis of the potential drivers of seasonality in COVID-19 transmission dynamics in 409 locations across 26 countries 

2021 ◽  
Author(s):  
Rachel Lowe ◽  
Ben Armstrong ◽  
Sam Abbott ◽  
Sophie Meakin ◽  
Kathleen O'Reilly ◽  
...  
Keyword(s):  
Early Phase ◽  
Time Window ◽  
Meteorological Factors ◽  
Absolute Humidity ◽  
Nonlinear Effects ◽  
Weather Conditions ◽  
Transmission Dynamics ◽  
Ecological Modelling ◽  
Government Interventions ◽  
Socio Demographic Factors

<p>More than a year since its emergence, there is conflicting evidence on the potential influence of weather conditions on SARS-CoV-2 transmission dynamics. We used a two-stage ecological modelling approach to estimate weather-dependent signatures in SARS-CoV-2 transmission in the early phase of the pandemic, using a dataset of 3 million COVID-19 cases reported until 31 May 2020, spanning 409 locations in 26 countries. We calculated the effective reproduction number (R<sub>e</sub>) over a location-specific early-phase time-window of 10-20 days, for which local transmission had been established but before non-pharmaceutical  interventions had become established as measured by the OxCGRT Government Response Index. We calculated mean levels of meteorological factors, including temperature and humidity observed in the same time window used to calculate R<sub>e</sub>.  Using a multilevel meta-regression approach, we modelled nonlinear effects of meteorological factors,  while accounting for government interventions and socio-demographic factors. A weak non-monotonic association between temperature, absolute humidity and R<sub>e</sub> was identified, with a decrease in R<sub>e</sub> of 0.087 (95% CI: 0.025; 0.148) between mean temperature of 10.2°C (maximum) and 20°C (minimum) and a decrease in R<sub>e</sub> of 0.061 (95% CI: 0.011; 0.111) between absolute humidity of 6.6 g/m3 (maximum) and 11 g/m3 (minimum). However, government interventions explained twice as much of the variation in R<sub>e</sub> compared meteorological factors. We find little evidence of meteorological conditions having influenced the early stages of local epidemics, and conclude that population behaviour and governmental intervention are more important drivers of transmission.</p>

scholarly journals A cross-sectional analysis of meteorological factors and SARS-CoV-2 transmission in 409 cities across 26 countries

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Francesco Sera ◽  
Ben Armstrong ◽  
Sam Abbott ◽  
Sophie Meakin ◽  
Kathleen O’Reilly ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Meteorological Factors ◽  
Cross Sectional ◽  
Government Interventions ◽  
Government Response ◽  
Mean Temperature ◽  
Conflicting Evidence ◽  
The Government ◽  
Sectional Analysis ◽  
Socio Economic Factors

AbstractThere is conflicting evidence on the influence of weather on COVID-19 transmission. Our aim is to estimate weather-dependent signatures in the early phase of the pandemic, while controlling for socio-economic factors and non-pharmaceutical interventions. We identify a modest non-linear association between mean temperature and the effective reproduction number (Re) in 409 cities in 26 countries, with a decrease of 0.087 (95% CI: 0.025; 0.148) for a 10 °C increase. Early interventions have a greater effect on Re with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by government interventions is 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and government interventions are more important drivers of transmission.

scholarly journals COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

2020 ◽  
Author(s):  
Mohamed Aziz Bhouri ◽  
Francisco Sahli Costabal ◽  
Hanwen Wang ◽  
Kevin Linka ◽  
Mathias Peirlinck ◽  
...  
Keyword(s):  
Deep Learning ◽  
Social Behavior ◽  
Reproduction Number ◽  
Time Window ◽  
Human Mobility ◽  
Government Interventions ◽  
Learning Framework ◽  
Mobility Data ◽  
Probabilistic Forecasts ◽  
The Us

This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Strikingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the basic reproduction number. The model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously inform policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

scholarly journals Ranking the effectiveness of worldwide COVID-19 government interventions

2020 ◽  
Author(s):  
Nils Haug ◽  
Lukas Geyrhofer ◽  
Alessandro Londei ◽  
Elma Dervic ◽  
Amélie Desvars-Larrive ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Local Context ◽  
Computational Techniques ◽  
Government Interventions ◽  
Suitable Combination ◽  
The Impact ◽  
First Time ◽  
Pharmaceutical Interventions ◽  
Country Specific ◽  
Modelling Approach

Assessing the effectiveness of Non-Pharmaceutical Interventions (NPIs) to mitigate the spread of SARS-CoV-2 is critical to inform future preparedness response plans. Here we quantify the impact of 6,068 hierarchically coded NPIs implemented in 79 territories on the effective reproduction number, Rt, of COVID-19. We propose a novel modelling approach that combines four computational techniques merging for the first time statistical, inference and artificial intelligence tools. We validate our findings with two external datasets with 48,000 additional NPIs from 226 countries. Our results indicate that a suitable combination of NPIs is necessary to curb the spread of the virus. Less intrusive and costly NPIs can be as effective as more intrusive, drastic, ones, e.g., a national lockdown. Using country-specific what-if scenarios we assess how the effectiveness of NPIs depends on the local context such as timing of their adoption, opening the way for forecasting the effectiveness of future interventions.

scholarly journals COVID-19 reproduction number and non-pharmaceutical interventions in Lithuania

2021 ◽  
Vol 62 ◽  
pp. 27-37
Author(s):  
Ramunė Vaišnorė ◽  
Audronė Jakaitienė
Keyword(s):  
Social Life ◽  
Reproduction Number ◽  
Data Governance ◽  
State Data ◽  
Incidence Data ◽  
Government Response ◽  
The World ◽  
The Republic ◽  
Positive Effect ◽  
Pharmaceutical Interventions

Currently the world is threatened by a global COVID-19 pandemic and it has induced crisis creating a lot of disruptions in the healthcare system, social life and economy. In this article we present the analysis of COVID-19 situation in Lithuania and it's municipalities taking into consideration the effect of non-pharmaceutical interventions on the reproduction number. We have analysed the period from 20/03/2020 to 20/06/2021 covering two quarantines applied in Lithuania. We calculated the reproduction number using the incidence data provided by State Data Governance Information System, while the information for applied non-pharmaceutical interventions was extracted from Oxford COVID-19 Government Response Tracker and the COVID-19 website of Government of the Republic of Lithuania. The positive effect of applied non-pharmaceutical interventions on reproduction number was observed when internal movement ban was applied in 16/12/2020 during the second quarantine in Lithuania.

scholarly journals Analysis of Non-Pharmaceutical Interventions Impacts on COVID-19 Pandemic in Iran

2021 ◽  
Author(s):  
Sara Saadatmand ◽  
Khodakaram Salimifard ◽  
Reza Mohammadi
Keyword(s):  
Reproduction Number ◽  
Generic Model ◽  
Government Interventions ◽  
Before And After ◽  
Huge Impact ◽  
Iranian Government ◽  
The Government ◽  
Nonpharmaceutical Interventions ◽  
Pharmaceutical Interventions

Abstract The COVID-19 pandemic has had a huge impact on people's health, and countries' infrastructures around the globe. Iran was one of the first countries that experienced the vast prevalence of the coronavirus outbreak. Iranian government applied various nonpharmaceutical interventions to eradicate the epidemic in different periods. To evaluate the effectiveness of applied policies, the number of cases and death before and after the interventions studied and the effective reproduction number of the infection was analyzed under various scenarios. The SEIR generic model was applied to capture the dynamic of the pandemic in Iran. It is assumed that changes in reproduction number are responses to interventions. Depending on how responsive people to the government interventions, the effectiveness of each intervention has been investigated. Based on the model results, the peak of the total number of infected individuals will occur around the end of May and the start of June 2021. It is revealed that the outbreak had been able to be smoothed if the government had continued the full lockdown and strict quarantine. The result will allow for the assessment of the effects of different government interventions in new outbreaks.

scholarly journals Role of meteorological factors in the transmission of SARS-CoV-2 in the United States

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yiqun Ma ◽  
Sen Pei ◽  
Jeffrey Shaman ◽  
Robert Dubrow ◽  
Kai Chen
Keyword(s):  
Ultraviolet Radiation ◽  
Air Temperature ◽  
Reproduction Number ◽  
Meteorological Factors ◽  
The United States ◽  
Meteorological Conditions ◽  
Specific Humidity ◽  
Low Levels ◽  
The Relationship

AbstractImproved understanding of the effects of meteorological conditions on the transmission of SARS-CoV-2, the causative agent for COVID-19 disease, is needed. Here, we estimate the relationship between air temperature, specific humidity, and ultraviolet radiation and SARS-CoV-2 transmission in 2669 U.S. counties with abundant reported cases from March 15 to December 31, 2020. Specifically, we quantify the associations of daily mean temperature, specific humidity, and ultraviolet radiation with daily estimates of the SARS-CoV-2 reproduction number (Rt) and calculate the fraction of Rt attributable to these meteorological conditions. Lower air temperature (within the 20–40 °C range), lower specific humidity, and lower ultraviolet radiation were significantly associated with increased Rt. The fraction of Rt attributable to temperature, specific humidity, and ultraviolet radiation were 3.73% (95% empirical confidence interval [eCI]: 3.66–3.76%), 9.35% (95% eCI: 9.27–9.39%), and 4.44% (95% eCI: 4.38–4.47%), respectively. In total, 17.5% of Rt was attributable to meteorological factors. The fractions attributable to meteorological factors generally were higher in northern counties than in southern counties. Our findings indicate that cold and dry weather and low levels of ultraviolet radiation are moderately associated with increased SARS-CoV-2 transmissibility, with humidity playing the largest role.

scholarly journals An agent based model for assessing transmission dynamics and health systems burden for COVID-19

2021 ◽  
Vol 24 (3) ◽  
pp. 1735
Author(s):  
Narassima M. S. ◽  
Anbuudayasankar S. P. ◽  
Guru Rajesh Jammy ◽  
Rashmi Pant ◽  
Lincoln Choudhury ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Health Systems ◽  
Time Window ◽  
Disease Model ◽  
Transmission Dynamics ◽  
Future Trends ◽  
Agent Based ◽  
Simulation Techniques ◽  
Critical Patients ◽  
Pharmaceutical Interventions

<span>Coronavirus disease of 2019 </span><span>(COVID-19) pandemic has caused over <br /> 230 million infections with more than 4 million deaths worldwide. Researches have been using various mathematical and simulation techniques to estimate the future trends of the pandemic to help the policymakers and healthcare fraternity. Agent-based models (ABM) could provide accurate projections than the compartmental models that have been largely used. The present study involves a simulation of ABM using a synthetic population from India to analyze the effects of interventions on the spread of the disease. A disease model with various states representing the possible progression of the disease was developed and simulated using AnyLogic. The results indicated that imposing stricter non-pharmaceutical interventions (NPI) lowered the peak values of infections, the proportion of critical patients, and the deceased. Stricter interventions offer a larger time window for the healthcare fraternity to enhance preparedness. The findings of this research could act as a start-point to understand the benefits of ABM-based models for projecting infectious diseases and analyzing the effects of NPI imposed.</span>

Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics (Preprint)

2020 ◽  
Author(s):  
Mario Coccia
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Critical Role ◽  
Meteorological Condition ◽  
Meteorological Conditions ◽  
Lessons Learned ◽  
Transmission Dynamics ◽  
Critical Threshold ◽  
Viral Infectivity ◽  
Low Wind Speed

BACKGROUND Coronavirus disease 2019 (COVID-19) is viral infection that generates a severe acute respiratory syndrome with serious pneumonia that may result in progressive respiratory failure and death. OBJECTIVE This study has two goals. The first is to explain the main factors determining the diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats with of accelerated viral infectivity in society. METHODS Correlation and regression analyses on on data of N=55 Italian province capitals, and data of infected individuals at as of April 2020. RESULTS The main results are: o The accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution. o Hinterland cities have average days of exceeding the limits set for PM10 (particulate matter 10 micrometers or less in diameter) equal to 80 days, and an average number of infected more than 2,000 individuals as of April 1st, 2020, coastal cities have days of exceeding the limits set for PM10 equal to 60 days and have about 700 infected in average. o Cities that average number of 125 days exceeding the limits set for PM10, last year, they have an average number of infected individual higher than 3,200 units, whereas cities having less than 100 days (average number of 48 days) exceeding the limits set for PM10, they have an average number of about 900 infected individuals. o The results reveal that accelerated transmission dynamics of COVID-19 in specific environments is due to two mechanisms given by: air pollution-to-human transmission and human-to-human transmission; in particular, the mechanisms of air pollution-to-human transmission play a critical role rather than human-to-human transmission. o The finding here suggests that to minimize future epidemic similar to COVID-19, the max number of days per year in which cities can exceed the limits set for PM10 or for ozone, considering their meteorological condition, is less than 50 days. After this critical threshold, the analytical output here suggests that environmental inconsistencies because of the combination between air pollution and meteorological conditions (with high moisture%, low wind speed and fog) trigger a take-off of viral infectivity (accelerated epidemic diffusion) with damages for health of population, economy and society. CONCLUSIONS Considering the complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity, lessons learned for COVID-19 have to be applied for a proactive socioeconomic strategy to cope with future epidemics, especially an environmental policy based on reduction of air pollution mainly in hinterland zones of countries, having low wind speed, high percentage of moisture and fog that create an environment that can damage immune system of people and foster a fast transmission of viral infectivity similar to the COVID-19. CLINICALTRIAL not applicable

scholarly journals Despite vaccination, China needs non-pharmaceutical interventions to prevent widespread outbreaks of COVID-19 in 2021

2021 ◽  
Author(s):  
Juan Yang ◽  
Valentina Marziano ◽  
Xiaowei Deng ◽  
Giorgio Guzzetta ◽  
Juanjuan Zhang ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Synergetic Effect ◽  
Data Driven ◽  
Natural Immunity ◽  
Epidemic Threshold ◽  
Policy Makers ◽  
Large Populations ◽  
One Year ◽  
Pharmaceutical Interventions

AbstractCOVID-19 vaccination is being conducted in over 200 countries and regions to control SARS-CoV-2 transmission and return to a pre-pandemic lifestyle. However, understanding when non-pharmaceutical interventions (NPIs) can be lifted as immunity builds up remains a key question for policy makers. To address this, we built a data-driven model of SARS-CoV-2 transmission for China. We estimated that, to prevent the escalation of local outbreaks to widespread epidemics, stringent NPIs need to remain in place at least one year after the start of vaccination. Should NPIs alone be capable of keeping the reproduction number (Rt) around 1.3, the synergetic effect of NPIs and vaccination could reduce the COVID-19 burden by up to 99% and bring Rt below the epidemic threshold in about 9 months. Maintaining strict NPIs throughout 2021 is of paramount importance to reduce COVID-19 burden while vaccines are distributed to the population, especially in large populations with little natural immunity.

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