Impacts of Social and Economic Factors on the Transmission of Coronavirus Disease 2019 (COVID-19) in China

Abstract

This paper examines the role of various socioeconomic factors in mediating the local and cross-city transmissions of the novel coronavirus 2019 in China. We implement a machine learning approach to select instrumental variables that strongly predict virus transmission among the rich exogenous weather characteristics. Our 2SLS estimates show that the stringent quarantine, massive lockdown and other public health measures imposed in late January significantly reduced the transmission rate of COVID-19. By early February, the virus spread had been contained. While many socioeconomic factors mediate the virus spread, a robust government response since late January played a determinant role in the containment of the virus. We also demonstrate that the actual population flow from the outbreak source poses a higher risk to the destination than other factors such as geographic proximity and similarity in economic conditions. The results have rich implications for ongoing global efforts in containment of COVID-19.

Global economic footprint of the COVID-19 pandemic

“…In the CN scenario, containment scenarios ranging from 2 to 6 months in duration and 20% to 80% in strictness are explored. In the NH scenario set, it is assumed that containment in China lasts 2 months at 80% strictness from January to March 14 , and containment in other affected countries ranges between 20% and 80% in strictness and 2 and 6 months in duration (from March onwards). In the global scenario (GB), we assume that China maintains containment measures for The first insight from the model is that the global cost of the pandemics depends foremost on the number of affected countries, and then on the required duration of containment policies; in contrast, the strictness of these policies is comparatively less important.…”

Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China

“…Given that the 2019-nCoV has rapidly spread worldwide due to human travel and caused severe illness and significant mortality, it is therefore essential to understand the impact of various control measures on human mobility and the virus transmission. Qiu et al (2020) apply machine learning tools and use exogenous temperature, wind speed, and precipitation in the preceding third and fourth weeks as the instruments to show that the massive lockdown and other control measures significantly reduced the virus transmission. Their results highlight that the population outflow from the outbreak source city poses higher risks to the destination cities than other social and economic factors, such as geographic proximity and similarity in economic conditions.…”

Prevalência e aspectos epidemiológicos da COVID-19 na 9ª Região de Saúde de Alagoas

“…Ao analisar a razão de chance entre os óbitos por COVID-19 e as principais comorbidades relatadas pelos pacientes, o Odds Ratio mostrou que pessoas com diabetes têm 8,7 vezes mais chances (OR = 8,7; IC 95% = 6,6;11,4 p < 0,000) de evoluir para óbito; que pessoas com hipertensão têm 7,4 vezes de chance (OR = 7,4; IC 95% = 4,6;11,9 p < 0,000); pessoas com doenças cardiovasculares têm 3,3 vezes de chance (OR = 3,3; IC 95% = 2,4;4,5 p < 0,000); enquanto pessoas com doença respiratória crônica têm 2,0 vezes de chance (OR = 2,0; IC 95% = 1,1;3,7 p < 0,016); e pessoas com asma tem 0,3 vezes de chance (OR = 0,3; IC 95% = 0,1;08 p < 0,009) de ir a óbito (tabela 5). A expansão da COVID-19 se deve, principalmente, às características de disseminação e transmissibilidade do SARS-CoV-2, podendo existir diversos fatores que contribuem para o aumento do número de casos, como os determinantes sociais, culturais e ambientais de cada região 10,11 . Esses fatores intrínsecos de cada região podem explicar o maior número de casos e maior prevalência para as cidades de Santana do Ipanema e Palestina, respectivamente.…”