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1.
Travel Med Infect Dis ; 47: 102317, 2022.
Article in English | MEDLINE | ID: covidwho-1815224

ABSTRACT

Rapid rise of population migration is a defining feature of the 21st century due to the impact of climate change, political instability, and socioeconomic downturn. Over the last decade, an increasing number of migrant peoples travel across the Americas to reach the United States seeking asylum or cross the border undocumented in search of economic opportunities. In this journey, migrant people experience violations of their human rights, hunger, illness, violence and have limited access to medical care. In the 'Divine Comedy', the Italian poet Dante Alighieri depicts his allegorical pilgrimage across Hell and Purgatory to reach Paradise. More than 700 years after its publication, Dante's poem speaks to the present time and the perilious journey of migrant peoples to reach safehavens. By exploring the depths and heights of the human condition, Dante's struggles resonate with the multiple barriers and the unfathomable experiences faced by migrant peoples in transit across South, Central, and North America to reach the United States. Ensuring the safety of migrant peoples across the Americas and elsewhere, and attending to their health needs during their migratory paths represent modern priorities to reduce social injustices and achieving health equity.


Subject(s)
Transients and Migrants , Americas , Developing Countries , Humans , Italy , Population Dynamics , United States
2.
Front Public Health ; 10: 883472, 2022.
Article in English | MEDLINE | ID: covidwho-1809632
3.
Soc Work ; 67(3): 218-227, 2022 Jun 20.
Article in English | MEDLINE | ID: covidwho-1806581

ABSTRACT

From the point of apprehension by U.S. Customs and Border Protection at the U.S.-Mexican border to their reunification with sponsors in U.S. communities, unaccompanied children (UC) face political, social, and economic conditions, heightening their risk for mental and physical health burdens that may be exacerbated during the COVID-19 pandemic. Such risk underscores the importance of social work practice and advocacy for the improved treatment and experiences of UC. This article uses a structural vulnerability conceptual lens to summarize the existing literature regarding UC and argues that UC's liminal immigration status, economic precarity, and lack of healthcare access place this group at high structural vulnerability during the pandemic. Further, this article identifies and describes three contexts of structural vulnerability of UC that are important points of social work intervention: (1) at the border, where migrant children are denied their legal right to seek protection; (2) in detention and shelter facilities; and (3) during reunification with sponsors. This article concludes with important practice and policy opportunities for social workers to pursue to obtain social justice for an important and highly vulnerable migrant child population.


Subject(s)
COVID-19 , Transients and Migrants , COVID-19/epidemiology , Child , Humans , Pandemics , Population Dynamics , Social Work
4.
Int J Environ Res Public Health ; 19(5)2022 Mar 07.
Article in English | MEDLINE | ID: covidwho-1732040

ABSTRACT

Most vulnerable individuals are particularly affected by the COVID-19 pandemic. This study takes place in a large city in France. The aim of this study is to describe the mobility of the homeless population at the beginning of the health crisis and to analyze its impact in terms of COVID-19 prevalence. From June to August 2020 and September to December 2020, 1272 homeless people were invited to be tested for SARS-CoV-2 antibodies and virus and complete questionnaires. Our data show that homeless populations are sociologically different depending on where they live. We show that people that were living on the street were most likely to be relocated to emergency shelters than other inhabitants. Some neighborhoods are points of attraction for homeless people in the city while others emptied during the health crisis, which had consequences for virus circulation. People with a greater number of different dwellings reported became more infected. This first study of the mobility and epidemiology of homeless people in the time of the pandemic provides unique information about mobility mapping, sociological factors of this mobility, mobility at different scales, and epidemiological consequences. We suggest that homeless policies need to be radically transformed since the actual model exposes people to infection in emergency.


Subject(s)
COVID-19 , Homeless Persons , COVID-19/epidemiology , Humans , Pandemics , Population Dynamics , SARS-CoV-2
6.
Sci Rep ; 12(1): 370, 2022 01 10.
Article in English | MEDLINE | ID: covidwho-1617000

ABSTRACT

COVID-19 outbreaks have had high mortality in low- and middle-income countries such as Ecuador. Human mobility is an important factor influencing the spread of diseases possibly leading to a high burden of disease at the country level. Drastic control measures, such as complete lockdown, are effective epidemic controls, yet in practice one hopes that a partial shutdown would suffice. It is an open problem to determine how much mobility can be allowed while controlling an outbreak. In this paper, we use statistical models to relate human mobility to the excess death in Ecuador while controlling for demographic factors. The mobility index provided by GRANDATA, based on mobile phone users, represents the change of number of out-of-home events with respect to a benchmark date (March 2nd, 2020). The study confirms the global trend that more men are dying than expected compared to women, and that people under 30 show less deaths than expected, particularly individuals younger than 20 with a death rate reduction between 22 and 27%. The weekly median mobility time series shows a sharp decrease in human mobility immediately after a national lockdown was declared on March 17, 2020 and a progressive increase towards the pre-lockdown level within two months. Relating median mobility to excess deaths shows a lag in its effect: first, a decrease in mobility in the previous two to three weeks decreases excess death and, more novel, we found an increase of mobility variability four weeks prior increases the number of excess deaths.


Subject(s)
COVID-19/mortality , Cause of Death , Communicable Disease Control/statistics & numerical data , Transportation/statistics & numerical data , Travel/statistics & numerical data , Adult , Algorithms , COVID-19/epidemiology , COVID-19/virology , Communicable Disease Control/methods , Ecuador/epidemiology , Female , Geography , Humans , Male , Pandemics/prevention & control , Population Dynamics , Risk Factors , SARS-CoV-2/physiology , Survival Rate , Time Factors , Young Adult
7.
Lancet Digit Health ; 3(11): e716-e722, 2021 11.
Article in English | MEDLINE | ID: covidwho-1557380

ABSTRACT

BACKGROUND: Little is known about the effect of changes in mobility at the subcity level on subsequent COVID-19 incidence, which is particularly relevant in Latin America, where substantial barriers prevent COVID-19 vaccine access and non-pharmaceutical interventions are essential to mitigation efforts. We aimed to examine the longitudinal associations between population mobility and COVID-19 incidence at the subcity level across a large number of Latin American cities. METHODS: In this longitudinal ecological study, we compiled aggregated mobile phone location data, daily confirmed COVID-19 cases, and features of urban and social environments to analyse population mobility and COVID-19 incidence at the subcity level among cities with more than 100 000 inhabitants in Argentina, Brazil, Colombia, Guatemala, and Mexico, from March 2 to Aug 29, 2020. Spatially aggregated mobile phone data were provided by the UN Development Programme in Latin America and the Caribbean and Grandata; confirmed COVID-19 cases were from national government reports and population and socioeconomic factors were from the latest national census in each country. We used mixed-effects negative binomial regression for a time-series analysis, to examine longitudinal associations between weekly mobility changes from baseline (prepandemic week of March 2-9, 2020) and subsequent COVID-19 incidence (lagged by 1-6 weeks) at the subcity level, adjusting for urban environmental and socioeconomic factors (time-invariant educational attainment, residential overcrowding, population density [all at the subcity level], and country). FINDINGS: We included 1031 subcity areas, representing 314 Latin American cities, in Argentina (107 subcity areas), Brazil (416), Colombia (82), Guatemala (20), and Mexico (406). In the main adjusted model, we observed an incidence rate ratio (IRR) of 2·35 (95% CI 2·12-2·60) for COVID-19 incidence per log unit increase in the mobility ratio (vs baseline) during the previous week. Thus, 10% lower weekly mobility was associated with 8·6% (95% CI 7·6-9·6) lower incidence of COVID-19 in the following week. This association gradually weakened as the lag between mobility and COVID-19 incidence increased and was not different from null at a 6-week lag. INTERPRETATION: Reduced population movement within a subcity area is associated with a subsequent decrease in COVID-19 incidence among residents of that subcity area. Policies that reduce population mobility at the subcity level might be an effective COVID-19 mitigation strategy, although they should be combined with strategies that mitigate any adverse social and economic consequences of reduced mobility for the most vulnerable groups. FUNDING: Wellcome Trust. TRANSLATION: For the Spanish translation of the abstract see Supplementary Materials section.


Subject(s)
COVID-19/epidemiology , Population Dynamics , Poverty , COVID-19/therapy , COVID-19 Vaccines , Cell Phone , Cities , Health Services Accessibility , Humans , Incidence , Latin America/epidemiology , Longitudinal Studies , Pandemics , SARS-CoV-2
8.
Int J Environ Res Public Health ; 18(23)2021 11 30.
Article in English | MEDLINE | ID: covidwho-1542560

ABSTRACT

The health of migrants and refugees, which has long been a cause for concern, has come under greatly increased pressure in the last decade. Against a background where the world has witnessed the largest numbers of migrants in history, the advent of the COVID-19 pandemic has stretched the capacities of countries and of aid, health and relief organizations, from global to local levels, to meet the human rights and pressing needs of migrants and refugees for access to health care and to public health measures needed to protect them from the pandemic. The overview in this article of the situation in examples of middle-income countries that have hosted mass migration in recent years has drawn on information from summaries presented in an M8 Alliance Expert Meeting, from peer-reviewed literature and from reports from international agencies concerned with the status and health of migrants and refugees. The multi-factor approach developed here draws on perspectives from structural factors (including rights, governance, policies and practices), health determinants (including economic, environmental, social and political, as well as migration itself as a determinant) and the human security framework (defined as "freedom from want and fear and freedom to live in dignity" and incorporating the interactive dimensions of health, food, environmental, economic, personal, community and political security). These integrate as a multi-component 'ecological perspective' to examine the legal status, health rights and access to health care and other services of migrants and refugees, to mark gap areas and to consider the implications for improving health security both for them and for the communities in countries in which they reside or through which they transit.


Subject(s)
COVID-19 , Refugees , Transients and Migrants , Demography , Emigration and Immigration , Health Services Accessibility , Human Rights , Humans , Pandemics , Population Dynamics , SARS-CoV-2
9.
Vet Rec ; 189(10): 386, 2021 11.
Article in English | MEDLINE | ID: covidwho-1525487
10.
Nat Commun ; 12(1): 6440, 2021 11 08.
Article in English | MEDLINE | ID: covidwho-1506955

ABSTRACT

Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19. We evaluate the model on 14 forecast dates between 2020/10/25 and 2021/01/24 over one- to four-week prediction horizons. Comparing our predictions with a Baseline model developed by the COVID-19 Forecast Hub indicates an average 6.46% improvement in prediction Mean Absolute Errors (MAE) over the two-week prediction horizon up to 20.22% improvement in the four-week prediction horizon, pointing to the strong predictive power of our model in the longer prediction horizons.


Subject(s)
COVID-19/epidemiology , Cell Phone Use/statistics & numerical data , COVID-19/transmission , COVID-19/virology , Forecasting , Humans , Machine Learning , Models, Statistical , Population Dynamics , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , Spatio-Temporal Analysis
11.
Sci Rep ; 11(1): 21715, 2021 11 05.
Article in English | MEDLINE | ID: covidwho-1504467

ABSTRACT

Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.


Subject(s)
COVID-19/epidemiology , Deep Learning , Neural Networks, Computer , Algorithms , Geography , Humans , Machine Learning , Memory, Short-Term , Models, Statistical , Monte Carlo Method , Population Dynamics , Public Health Informatics , Reproducibility of Results , SARS-CoV-2 , Time Factors , United States/epidemiology
12.
Sci Rep ; 11(1): 21707, 2021 11 04.
Article in English | MEDLINE | ID: covidwho-1504388

ABSTRACT

We investigate the connection between the choice of transportation mode used by commuters and the probability of COVID-19 transmission. This interplay might influence the choice of transportation means for years to come. We present data on commuting, socioeconomic factors, and COVID-19 disease incidence for several US metropolitan areas. The data highlights important connections between population density and mobility, public transportation use, race, and increased likelihood of transmission. We use a transportation model to highlight the effect of uncertainty about transmission on the commuters' choice of transportation means. Using multiple estimation techniques, we found strong evidence that public transit ridership in several US metro areas has been considerably impacted by COVID-19 and by the policy responses to the pandemic. Concerns about disease transmission had a negative effect on ridership, which is over and above the adverse effect from the observed reduction in employment. The COVID-19 effect is likely to reduce the demand for public transport in favor of lower density alternatives. This change relative to the status quo will have implications for fuel use, congestion, accident frequency, and air quality. More vulnerable communities might be disproportionally affected as a result. We point to the need for additional studies to further quantify these effects and to assist policy in planning for the post-COVID-19 transportation future.


Subject(s)
COVID-19/transmission , Transportation/economics , Transportation/statistics & numerical data , Cities , Employment/trends , Humans , Motor Vehicles/economics , Motor Vehicles/statistics & numerical data , Pandemics , Population Density , Population Dynamics/trends , SARS-CoV-2/pathogenicity , Socioeconomic Factors , Transportation/methods , United States/epidemiology
13.
PLoS One ; 16(10): e0259037, 2021.
Article in English | MEDLINE | ID: covidwho-1496524

ABSTRACT

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines a well-established approach from transportation modelling that uses person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of different room sizes, air exchange rates, disease import, changed activity participation rates over time (coming from mobility data), masks, indoors vs. outdoors leisure activities, and of contact tracing. It is validated against the infection dynamics in Berlin (Germany). The model can be used to understand the contributions of different activity types to the infection dynamics over time. It predicts the effects of contact reductions, school closures/vacations, masks, or the effect of moving leisure activities from outdoors to indoors in fall, and is thus able to quantitatively predict the consequences of interventions. It is shown that these effects are best given as additive changes of the reproduction number R. The model also explains why contact reductions have decreasing marginal returns, i.e. the first 50% of contact reductions have considerably more effect than the second 50%. Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, or consequences of wearing masks in certain situations. The results can be used to inform political decisions.


Subject(s)
COVID-19/prevention & control , Communicable Disease Control/methods , Contact Tracing/methods , Berlin , COVID-19/metabolism , Cell Phone/trends , Computer Simulation , Germany , Hand Disinfection/trends , Humans , Masks/trends , Models, Theoretical , Physical Distancing , Population Dynamics/trends , SARS-CoV-2/pathogenicity , Systems Analysis
15.
J Environ Manage ; 302(Pt A): 113949, 2022 Jan 15.
Article in English | MEDLINE | ID: covidwho-1466606

ABSTRACT

Social distancing policies (SDPs) implemented in response to the COVID-19 pandemic have led to temporal and spatial shifts in water demand across cities. Water utilities need to understand these demand shifts to respond to potential operational and water-quality issues. Aided by a fixed-effects model of citywide water demand in Austin, Texas, we explore the impacts of various SDPs (e.g., time after the stay home-work safe order, reopening phases) using daily demand data gathered between 2013 and 2020. Our approach uses socio-technical determinants (e.g., climate, water conservation policy) with SDPs to model water demand, while accounting for spatial and temporal effects (e.g., geographic variations, weekday patterns). Results indicate shifts in behavior of residential and nonresidential demands that offset the change at the system scale, demonstrating a spatial redistribution of water demand after the stay home-work safe order. Our results show that some phases of Texas's reopening phases had statistically significant relationships to water demand. While this yielded only marginal net effects on overall demand, it underscores behavioral changes in demand at sub-system spatial scales. Our discussions shed light on SDPs' impacts on water demand. Equipped with our empirical findings, utilities can respond to potential vulnerabilities in their systems, such as water-quality problems that may be related to changes in water pressure in response to demand variations.


Subject(s)
COVID-19 , Water , Humans , Pandemics , Physical Distancing , Policy , Population Dynamics , SARS-CoV-2 , Water Supply
16.
Sci Rep ; 11(1): 19952, 2021 10 07.
Article in English | MEDLINE | ID: covidwho-1462028

ABSTRACT

The dynamic characterization of the COVID-19 outbreak is critical to implement effective actions for its control and eradication but the information available at a global scale is not sufficiently reliable to be used directly. Here, we develop a quantitative approach to reliably quantify its temporal evolution and controllability through the integration of multiple data sources, including death records, clinical parametrization of the disease, and demographic data, and we explicitly apply it to countries worldwide, covering 97.4% of the human population, and to states within the United States (US). The validation of the approach shows that it can accurately reproduce the available prevalence data and that it can precisely infer the timing of nonpharmaceutical interventions. The results of the analysis identified general patterns of recession, stabilization, and resurgence. The diversity of dynamic behaviors of the outbreak across countries is paralleled by those of states and territories in the US, converging to remarkably similar global states in both cases. Our results offer precise insights into the dynamics of the outbreak and an efficient avenue for the estimation of the prevalence rates over time.


Subject(s)
COVID-19/epidemiology , Basic Reproduction Number , Computer Simulation , Death Certificates , Demography , Disease Outbreaks , Global Health , Humans , Population Dynamics , SARS-CoV-2/isolation & purification , United States/epidemiology
17.
J Public Health (Oxf) ; 43(Suppl 3): iii29-iii33, 2021 12 08.
Article in English | MEDLINE | ID: covidwho-1440646

ABSTRACT

BACKGROUND: There is no prior study of the effect of mobility-limiting measures on the occurrence of COVID-19 in Iraq. OBJECTIVES: To determine the relationship between publicly available mobility index data and the growth ratio (GR) of COVID-19. METHOD: We used Google COVID-19 Community Mobility Reports to extract Iraq's mobility data and the official Ministry of Health COVID-19 statements. We used the data to calculate the Pearson's correlation coefficient and fit a linear regression model to determine the relationship between percentage change from the baseline in the mobility indices and the GR of COVID-19 in Iraq. RESULTS: There was a moderate positive correlation between each of the mobility indices except the residential index and COVID-19 GR in Iraq. The general linear model indicated that as each of the mobility indices increases by one unit, the GR of COVID19 increases by 0.002-0.003 except for the residential index. As the residential mobility index increases by one unit, the GR decreases by 0.009. All the findings were statistically significant (P-value < 0.0001). CONCLUSION: Mobility-limiting measures may be able to reduce the growth rate of COVID-19 moderately. Accordingly, mobility-limiting measures should be combined with other public control measures particularly mass mask use.


Subject(s)
COVID-19 , Correlation of Data , Humans , Iraq , Population Dynamics , SARS-CoV-2
18.
Sci Total Environ ; 806(Pt 1): 150406, 2022 Feb 01.
Article in English | MEDLINE | ID: covidwho-1415776

ABSTRACT

Wastewater surveillance has been widely implemented for monitoring of SARS-CoV-2 during the global COVID-19 pandemic, and near-to-source monitoring is of particular interest for outbreak management in discrete populations. However, variation in population size poses a challenge to the triggering of public health interventions using wastewater SARS-CoV-2 concentrations. This is especially important for near-to-source sites that are subject to significant daily variability in upstream populations. Focusing on a university campus in England, this study investigates methods to account for variation in upstream populations at a site with highly transient footfall and provides a better understanding of the impact of variable populations on the SARS-CoV-2 trends provided by wastewater-based epidemiology. The potential for complementary data to help direct response activities within the near-to-source population is also explored, and potential concerns arising due to the presence of heavily diluted samples during wet weather are addressed. Using wastewater biomarkers, it is demonstrated that population normalisation can reveal significant differences between days where SARS-CoV-2 concentrations are very similar. Confidence in the trends identified is strongest when samples are collected during dry weather periods; however, wet weather samples can still provide valuable information. It is also shown that building-level occupancy estimates based on complementary data aid identification of potential sources of SARS-CoV-2 and can enable targeted actions to be taken to identify and manage potential sources of pathogen transmission in localised communities.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Pandemics , Population Dynamics , Sewage , Universities , Waste Water , Wastewater-Based Epidemiological Monitoring
19.
Nat Microbiol ; 6(10): 1271-1278, 2021 10.
Article in English | MEDLINE | ID: covidwho-1402078

ABSTRACT

Genomics, combined with population mobility data, used to map importation and spatial spread of SARS-CoV-2 in high-income countries has enabled the implementation of local control measures. Here, to track the spread of SARS-CoV-2 lineages in Bangladesh at the national level, we analysed outbreak trajectory and variant emergence using genomics, Facebook 'Data for Good' and data from three mobile phone operators. We sequenced the complete genomes of 67 SARS-CoV-2 samples (collected by the IEDCR in Bangladesh between March and July 2020) and combined these data with 324 publicly available Global Initiative on Sharing All Influenza Data (GISAID) SARS-CoV-2 genomes from Bangladesh at that time. We found that most (85%) of the sequenced isolates were Pango lineage B.1.1.25 (58%), B.1.1 (19%) or B.1.36 (8%) in early-mid 2020. Bayesian time-scaled phylogenetic analysis predicted that SARS-CoV-2 first emerged during mid-February in Bangladesh, from abroad, with the first case of coronavirus disease 2019 (COVID-19) reported on 8 March 2020. At the end of March 2020, three discrete lineages expanded and spread clonally across Bangladesh. The shifting pattern of viral diversity in Bangladesh, combined with the mobility data, revealed that the mass migration of people from cities to rural areas at the end of March, followed by frequent travel between Dhaka (the capital of Bangladesh) and the rest of the country, disseminated three dominant viral lineages. Further analysis of an additional 85 genomes (November 2020 to April 2021) found that importation of variant of concern Beta (B.1.351) had occurred and that Beta had become dominant in Dhaka. Our interpretation that population mobility out of Dhaka, and travel from urban hotspots to rural areas, disseminated lineages in Bangladesh in the first wave continues to inform government policies to control national case numbers by limiting within-country travel.


Subject(s)
COVID-19/transmission , Cell Phone/statistics & numerical data , Genome, Viral/genetics , SARS-CoV-2/genetics , Social Media/statistics & numerical data , Bangladesh/epidemiology , Bayes Theorem , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , Disease Outbreaks/prevention & control , Disease Outbreaks/statistics & numerical data , Genomics , Health Policy/legislation & jurisprudence , Humans , Phylogeny , Population Dynamics/statistics & numerical data , SARS-CoV-2/classification , Travel/legislation & jurisprudence , Travel/statistics & numerical data
20.
Sci Rep ; 10(1): 16950, 2020 10 12.
Article in English | MEDLINE | ID: covidwho-1387452

ABSTRACT

The spread of SARS-COV-2 has affected many economic and social systems. This paper aims at estimating the impact on regional productive systems in Italy of the interplay between the epidemic and the mobility restriction measures put in place to contain the contagion. We focus then on the economic consequences of alternative lockdown lifting schemes. We leverage a massive dataset of human mobility which describes daily movements of over four million individuals in Italy and we model the epidemic spreading through a metapopulation SIR model, which provides the fraction of infected individuals in each Italian district. To quantify economic backslashes this information is combined with socio-economic data. We then carry out a scenario analysis to model the transition to a post-lockdown phase and analyze the economic outcomes derived from the interplay between (a) the timing and intensity of the release of mobility restrictions and (b) the corresponding scenarios on the severity of virus transmission rates. Using a simple model for the spreading disease and parsimonious assumptions on the relationship between the infection and the associated economic backlashes, we show how different policy schemes tend to induce heterogeneous distributions of losses at the regional level depending on mobility restrictions. Our work shed lights on how recovery policies need to balance the interplay between mobility flows of disposable workers and the diffusion of contagion.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Population Dynamics , Public Health/methods , Betacoronavirus , COVID-19 , Humans , Models, Biological , Movement , Pandemics , Quarantine/methods , SARS-CoV-2 , Travel
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