Meteorological factors against COVID-19 and the role of human mobility

In the vein of recent empirical literature, we reassessed the impact of weather factors on Covid-19 daily cases and fatalities in a panel of 37 OECD countries between 1st January and 27th July 2020. We considered five different meteorological factors. For the first time, we used a dynamic panel model and considered two different kinds of channels between climate and Covid-19 virus: direct/physical factors related to the survival and durability dynamics of the virus on surfaces and outdoors and indirect/social factors through human behaviour and individual mobility, such as walking or driving outdoors, to capture the impact of weather on social distancing and, thus, on Covid-19 cases and fatalities. Our work revealed that temperature, humidity and solar radiation, which has been clearly under considered in previous studies, significantly reduce the number of Covid-19 cases and fatalities. Indirect effects through human behaviour, i.e., correlations between temperature (or solar radiation) and human mobility, were significantly positive and should be considered to correctly assess the effects of climatic factors. Increasing temperature, humidity or solar radiation effects were positively correlated with increasing mobility effects on Covid-19 cases and fatalities. The net effect from weather on the Covid-19 outbreak will, thus, be the result of the physical/direct negative effect of temperature or solar radiation and the mobility/indirect positive effect due to the interaction between human mobility and those meterological variables. Reducing direct effects of temperature and solar radiation on Covid-19 cases and fatalities, when they were significant, were partly and slightly compensated for positive indirect effects through human mobility. Suitable control policies should be implemented to control mobility and social distancing even when the weather is favourable to reduce the spread of the Covid-19 virus.

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Social-distancing Fatigue: Evidence from Real-time Crowd-sourced Traffic Data

10.1101/2021.03.04.21252917 ◽

2021 ◽

Author(s):

Jenni A. Shearston ◽

Micaela E. Martinez ◽

Yanelli Nunez ◽

Markus Hilpert

Keyword(s):

Time Series ◽

Real Time ◽

Traffic Congestion ◽

Human Mobility ◽

Additive Models ◽

School Closure ◽

High Temporal Resolution ◽

Social Distancing ◽

The Impact ◽

At Home

ABSTRACTIntroductionTo mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and results in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies.MethodsWe used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions.ResultsMobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels.ConclusionUsing crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.GRAPHICAL ABSTRACT

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Influence of Meteorological Factors on the Potential Evapotranspiration in Yanhe River Basin, China

10.22541/au.161606525.58625543/v1 ◽

2021 ◽

Author(s):

yu luo ◽

Peng Gao ◽

Xingmin Mu

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

River Basin ◽

Air Temperature ◽

Hydrological Cycle ◽

Meteorological Factors ◽

Potential Evapotranspiration ◽

Sensitivity Coefficients ◽

The Impact

Potential evapotranspiration (ET) is an essential component of the hydrological cycle, and quantitative estimation of the influence of meteorological factors on ET can provide a scientific basis for studying the impact mechanisms of climate change. In the present research, the Penman-Monteith method was used to calculate ET. The Mann-Kendall statistical test with the inverse distance weighting were used to analyze the spatiotemporal characteristics of the sensitivity coefficients and contribution rates of meteorological factors to ET to identify the mechanisms underlying changing ET rates. The results showed that the average ET for the Yanhe River Basin, China from 1978–2017 was 935.92 mm. Save for a single location (Ganquan), ET increased over the study period. Generally, the sensitivity coefficients of air temperature (0.08), wind speed at 2 m (0.19), and solar radiation (0.42) were positive, while that of relative humidity was negative (-0.41), although significant spatiotemporal differences were observed. Increasing air temperature and solar radiation contributed 1.09% and 0.55% of the observed rising ET rates, respectively; whereas decreasing wind speed contributed -0.63%, and relative humidity accounted for -0.85%. Therefore, it was concluded that the decrease of relative humidity did not cause the observed ET increase in the basin. The predominant factor driving increasing ET was rising air temperatures, but this too varied significantly by location and time (intra- and interannually). Decreasing wind speed at Ganquan Station decreased ET by -9.16%, and was the primary factor underlying the observed, local “evaporation paradox.” Generally, increases in ET were driven by air temperature, wind speed and solar radiation, whereas decreases were derived from relative humidity.

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Disaster Risk Reduction

Oxford Research Encyclopedia of Politics ◽

10.1093/acrefore/9780190228637.013.1538 ◽

2020 ◽

Author(s):

David Alexander

Keyword(s):

Environmental Change ◽

Risk Reduction ◽

Natural Hazards ◽

Disaster Risk Reduction ◽

Human Mobility ◽

Modern Society ◽

Disaster Risk ◽

Physical Factors ◽

The Future ◽

The Impact

Disaster risk reduction (DRR, or disaster reduction) is an umbrella term for processes of preparing for, responding to, recovering from, and managing the risk of disasters. It refers primarily to the acts of setting the policy and strategic agendas for these tasks. It reflects a long-standing need to reorientate priorities from merely responding to disasters once they have struck to reducing or avoiding their impacts. To be achieved, DRR requires a combination of physical and social measures, with full participation of affected populations and other stakeholders. Academically, disasters have been studied systematically for more than 100 years. During this period, the emphasis has changed from analyzing natural hazards as the primary drivers of disaster to a more pluralistic approach in which vulnerability and exposure to hazards and threats are viewed as playing vitally important roles. Disasters can have natural, technological, social, or intentional (i.e., terrorism-related) causes, but they are increasingly composite events that involve combinations of factors. Hence there is now much emphasis on “natech” events, in which natural hazards affect technological systems, and cascading disasters, in which escalation points caused by interacting sources of vulnerability may have the power to make the secondary effects more important than the primary trigger. Root causes and contexts have assumed a greater salience in the explanation of disaster, which tends to involve complex interactions among social, economic, political, and physical factors. Resilience has come to the fore as a positive concept for organizing processes of DRR. It is usually defined as a mixture of adaptation to hazards and threats and the ability to resist or overcome the negative effects of disaster. DRR concepts and strategies have been mainstreamed in modern society by international action under the auspices of the United Nations and the Sendai Framework for DRR, 2015–2030. The challenges of applying UN frameworks include uncertainty about whether the underlying concepts are durable, whether they can be applied rigorously, whether they have enough support among policy and decisionmakers, and whether they can acquire a sound practical basis. The future of DRR depends on humanity’s ability to implement solutions to conflict, migration, and environmental change, not merely the impact of disasters per se. In an era in which population is rising, wealth disparities and human mobility are increasing, and environmental change has begun to lead to major upheavals, DRR has gone from being a rather esoteric, specialized field to one that is central to the future of human existence.

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Cumulative Effect of Temperature and Solar Radiation on Wheat Yield

Notulae Botanicae Horti Agrobotanici Cluj-Napoca ◽

10.15835/nbha3925406 ◽

2011 ◽

Vol 39 (2) ◽

pp. 146 ◽

Cited By ~ 8

Author(s):

Mukhtar AHMED ◽

Fayyaz-ul HASSAN

Keyword(s):

Solar Radiation ◽

Grain Yield ◽

Wheat Yield ◽

Cumulative Effect ◽

Adaptation Strategy ◽

Effect Of Temperature ◽

Grain Number ◽

Sowing Time ◽

The Impact ◽

Determinant Factor

The impact of temperature and solar radiations were studied as determinant factor for spring wheat grain yield. The data obtained at anthesis and maturity for grain number (GN), grain weight (GW) and grain yield (Y) were examined with mean temperature at anthesis (T1) and maturity (T2), solar radiation at anthesis (SR1) and maturity (SR2) and photothermal quotient (PTQ) at anthesis (PTQ1) and maturity (PTQ2). The data obtained was subjected to Statistica 8 software and scatter plot regression model was developed at 95% confidence interval with crop data and climate variables (T1, T2, SR1, SR2, PTQ1 and PTQ2). Results clearly indicated that yield remained directly proportional to solar radiation and temperature plus solar radiation (PTQ) while inversely to temperature under optimum other environmental resources. Direct relationship between PTQ and yield parameters confirmed that it determined crop yield and its management for variable environmental conditions need to be opted by adopting suitable sowing time as an adaptation strategy under changing climate.

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Impact of US vaccination strategy on COVID-19 wave dynamics

Scientific Reports ◽

10.1038/s41598-021-90539-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Corentin Cot ◽

Giacomo Cacciapaglia ◽

Anna Sigridur Islind ◽

María Óskarsdóttir ◽

Francesco Sannino

Keyword(s):

Renormalization Group ◽

Human Mobility ◽

Vaccination Campaign ◽

Vaccination Strategy ◽

Flight Data ◽

Social Distancing ◽

Wave Dynamics ◽

The Us ◽

The Impact ◽

Pandemic Wave

AbstractWe employ the epidemic Renormalization Group (eRG) framework to understand, reproduce and predict the COVID-19 pandemic diffusion across the US. The human mobility across different geographical US divisions is modelled via open source flight data alongside the impact of social distancing for each such division. We analyse the impact of the vaccination strategy on the current pandemic wave dynamics in the US. We observe that the ongoing vaccination campaign will not impact the current pandemic wave and therefore strict social distancing measures must still be enacted. To curb the current and the next waves our results indisputably show that vaccinations alone are not enough and strict social distancing measures are required until sufficient immunity is achieved. Our results are essential for a successful vaccination strategy in the US.

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Impact of small-area lockdowns for the control of the COVID-19 pandemic

10.1101/2020.05.05.20092106 ◽

2020 ◽

Cited By ~ 2

Author(s):

Cristóbal Cuadrado ◽

María José Monsalves ◽

Jean Gajardo ◽

María Paz Bertoglia ◽

Manuel Nájera ◽

...

Keyword(s):

Small Area ◽

Human Mobility ◽

Viral Transmission ◽

Educational Institutions ◽

Reproductive Number ◽

Urban Mobility ◽

Social Distancing ◽

Mobility Data ◽

Quasi Experimental ◽

The Impact

AbstractBackgroundCountries confronting the COVID-19 pandemic are implementing different social distancing strategies. We evaluated the impact of small-area lockdowns in Chile, aimed to reduce viral transmission while minimizing the population disrupted. The effectiveness of this intervention on the outbreak control is unknown.MethodsA natural experiment assessing the impact of small-area lockdowns between February 15th and April 25th, 2020. We used mobility data and official governmental reports to compare regions with small-area lockdowns versus regions without. The primary outcome was the mean difference in the effective reproductive number (Re) of COVID-19. Secondary outcomes were changes in mobility indicators. We used quasi-experimental methods for the analysis and examined the impact of other concurrent public health interventions to disentangle their effects.ResultsSmall-area lockdown produced a sizable reduction in human mobility, equivalent to an 11.4% reduction (95%CI −14.4% to −8.38%) in public transport and similar effects in other mobility indicators. Ten days after implementation, the small-area lockdown produced a reduction of the effective reproductive number (Re) of 0.86 (95%CI −1.70 to −0.02). School and university closures, implemented earlier, led to a 40% reduction in urban mobility. Closure of educational institutions resulted in an even greater Re reduction compared with small-area lockdowns.ConclusionsSmall-area lockdowns produced a reduction in mobility and viral transmission, but the effects were smaller than the early closures of schools and universities. Small-area lockdowns may have a relevant supporting role in reducing SARS-CoV-2 transmission and could be useful for countries considering scaling-down stricter social distancing interventions.

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Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics

10.1101/2020.06.05.20123760 ◽

2020 ◽

Author(s):

Aniruddha Adiga ◽

Lijing Wang ◽

Adam Sadilek ◽

Ashish Tendulkar ◽

Srinivasan Venkatramanan ◽

...

Keyword(s):

Human Mobility ◽

The United States ◽

Global Scale ◽

International Travel ◽

Population Mixing ◽

Government Interventions ◽

Social Distancing ◽

Epidemic Dynamics ◽

The Us ◽

The Impact

AbstractThis work quantifies the impact of interventions to curtail mobility and social interactions in order to control the COVID-19 pandemic. We analyze the change in world-wide mobility at multiple spatio-temporal resolutions – county, state, country – using an anonymized aggregate mobility map that captures population flows between geographic cells of size 5 km2. We show that human mobility underwent an abrupt and significant change, partly in response to the interventions, resulting in 87% reduction of international travel and up to 75% reduction of domestic travel. Taking two very different countries sampled from the global spectrum, we observe a maximum reduction of 42% in mobility across different states of the United States (US), and a 68% reduction across the states of India between late March and late April. Since then, there has been an uptick in flows, with the US seeing 53% increase and India up to 38% increase with respect to flows seen during the lockdown. As we overlay this global map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell – often before stay-at-home orders were issued. Further, in order to understand mixing within a region, we propose a new metric to quantify the effect of social distancing on the basis of mobility. We find that population mixing has decreased considerably as the pandemic has progressed and are able to measure this effect across the world. Finally, we carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. To our knowledge, this work is the first to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale.

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Is climate a curse or a bless in the Covid-19 virus fighting?

10.1101/2020.09.04.20182998 ◽

2020 ◽

Author(s):

Olivier DAMETTE ◽

Clement Mathonnat ◽

Stephane Goutte

Keyword(s):

Indirect Effects ◽

Time Perspective ◽

Meteorological Factors ◽

Climatic Factors ◽

Climatic Variables ◽

Human Behaviors ◽

Long Run ◽

Short Run ◽

Per Se ◽

The Impact

Faced with the global pandemic of Covid-19, we need to better understand the links between meteorological factors and the virus and investigate the existence of potential seasonal patterns. In the vein of a recent empirical literature, we reassess the impact of weather factors on Covid-19 daily cases in a panel of advanced and emerging countries between January the first and 28th May 2020. We consider 5 different meteorological factors and go further previous studies. In addition, we give a short-run and medium/long-run time perspective of the dramatic outcomes of the pandemic by both considering infected people (short-run) and fatalities (long-run). Our results reveal that the choice of delays and time perspective of the effects of climatic factors on the virus are crucial as well as Covid-19 outcomes can explain the discrepancies in the previous literature. For the first time, we use a dynamic panel model and consider two different kinds of channels between climate and Covid-19 virus: 1) direct/physical factors related to the survivals and durability dynamics of the virus in surfaces and outdoors and 2) an indirect factor through human behaviors and individual mobility - walking or driving outdoors - to capture the impact of climate on social distancing and thus on Covid-19 outcomes. Our model is estimated \emph{per se} two different estimators and persistence, delays in patterns, nonlinearities and numerous specifications changes are taken into account with many robustness checks. Our work reveal that temperatures and, more interestingly, solar radiation - that has been clearly undervalued in previous studies - are significant climatic drivers on Covid-19 outbreak. Indirect effects through human behaviors ie interrelationships between climatic variables and people mobility are significantly positive and should be considered to correctly assess the effects of climatic factors. Since climate is per se purely exogenous, climate tend to strengthen the effect of mobility on virus spread. The net effect from climate on Covid-19 outbreak will thus result from the direct negative effect of climatic variables and from the indirect effect due to the interaction between mobility and them. Direct negative effects from climatic factors on Covid-19 outcomes - when they are significant - are partly compensated by positive indirect effects through human mobility. Suitable control policies should be implemented to control the mobility and social distancing. \\

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Influence of Meteorological Factors on the Potential Evapotranspiration in Yanhe River Basin, China

Water ◽

10.3390/w13091222 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1222

Author(s):

Yu Luo ◽

Peng Gao ◽

Xingmin Mu

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

River Basin ◽

Air Temperature ◽

Hydrological Cycle ◽

Meteorological Factors ◽

Potential Evapotranspiration ◽

Sensitivity Coefficients ◽

The Impact

Potential evapotranspiration (ET0) is an essential component of the hydrological cycle, and quantitative estimation of the influence of meteorological factors on ET0 can provide a scientific basis for studying the impact mechanisms of climate change. In the present research, the Penman–Monteith method was used to calculate ET0. The Mann–Kendall statistical test with the inverse distance weighting were used to analyze the spatiotemporal characteristics of the sensitivity coefficients and contribution rates of meteorological factors to ET0 to identify the mechanisms underlying changing ET0 rates. The results showed that the average ET0 for the Yanhe River Basin, China from 1978–2017 was 935.92 mm. Save for a single location (Ganquan), ET0 increased over the study period. Generally, the sensitivity coefficients of air temperature (0.08), wind speed at 2 m (0.19), and solar radiation (0.42) were positive, while that of relative humidity was negative (−0.41), although significant spatiotemporal differences were observed. Increasing air temperature and solar radiation contributed 1.09% and 0.55% of the observed rising ET0 rates, respectively; whereas decreasing wind speed contributed −0.63%, and relative humidity accounted for −0.85%. Therefore, it was concluded that the decrease of relative humidity did not cause the observed ET0 increase in the basin. The predominant factor driving increasing ET0 was rising air temperatures, but this too varied significantly by location and time (intra- and interannually). Decreasing wind speed at Ganquan Station decreased ET0 by −9.16%, and was the primary factor underlying the observed, local “evaporation paradox”. Generally, increase in ET0 was driven by air temperature, wind speed and solar radiation, whereas decrease was derived from relative humidity.

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Human Mobility Restrictions and the Spread of the Novel Coronavirus (2019-nCoV) in China

10.1101/2020.03.24.20042424 ◽

2020 ◽

Cited By ~ 18

Author(s):

Hanming Fang ◽

Long Wang ◽

Yang Yang

Keyword(s):

Human Mobility ◽

Hubei Province ◽

The Novel ◽

Chinese Cities ◽

Social Distancing ◽

Spring Festival ◽

The Social ◽

Novel Coronavirus ◽

The Impact ◽

The City

AbstractWe quantify the causal impact of human mobility restrictions, particularly the lockdown of the city of Wuhan on January 23, 2020, on the containment and delay of the spread of the Novel Coronavirus (2019-nCoV). We employ a set of difference-in-differences (DID) estimations to disentangle the lockdown effect on human mobility reductions from other confounding effects including panic effect, virus effect, and the Spring Festival effect. We find that the lockdown of Wuhan reduced inflow into Wuhan by 76.64%, outflows from Wuhan by 56.35%, and within-Wuhan movements by 54.15%. We also estimate the dynamic effects of up to 22 lagged population inflows from Wuhan and other Hubei cities, the epicenter of the 2019-nCoV outbreak, on the destination cities’ new infection cases. We find, using simulations with these estimates, that the lockdown of the city of Wuhan on January 23, 2020 contributed significantly to reducing the total infection cases outside of Wuhan, even with the social distancing measures later imposed by other cities. We find that the COVID-19 cases would be 64.81% higher in the 347 Chinese cities outside Hubei province, and 52.64% higher in the 16 non-Wuhan cities inside Hubei, in the counterfactual world in which the city of Wuhan were not locked down from January 23, 2020. We also find that there were substantial undocumented infection cases in the early days of the 2019-nCoV outbreak in Wuhan and other cities of Hubei province, but over time, the gap between the officially reported cases and our estimated “actual” cases narrows significantly. We also find evidence that enhanced social distancing policies in the 63 Chinese cities outside Hubei province are effective in reducing the impact of population inflows from the epi-center cities in Hubei province on the spread of 2019-nCoV virus in the destination cities elsewhere.JEL CodesI18, I10.

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