Forecasting of COVID-19 infections in E7 countries and proposing some policies based on the Stringency Index

Abstract

COVID-19 infection data of Emerging 7 (E7) countries, namely Brazil, China, India, Indonesia, Mexico, Russia, and Turkey were described by an empirical model or a special case of this empirical model. Near-future forecasts were also performed. Moreover, the causalities between the Stringency Index’s indicators and total cases in E7 countries in COVID-19 period were examined. Countries were grouped as “stationary,” “transition,” and “exponential” based on the data and model fits. The proposed models produced good fits to the COVID-19 data of E7 countries and it was possible to predict the number of cases in the near future. Some policies to control total cases in E7 countries were also proposed in the final phase of this study based on the findings and forecasting in these countries.

COVID-19 pandemic in BRICS countries and its association with socio-economic and demographic characteristics, health vulnerability, resources, and policy response

“…In this study, we also found that countries with the most disadvantaged communities, vulnerable health system, and less resources (including health system as well water and sanitation) were associated with more COVID-19 cases in BRICS countries. Our results were consistent with previous findings [6,20]. It supported the evidence that socio-economic vulnerability might have impact on the disproportionate burden on states and municipalities.…”

“…Rocha et al [6] found that the initial spread of COVID-19 infections in the country was most affected by patterns of socio-economic vulnerability rather than population age structure and prevalence of existing chronic diseases. Kayral et al [20] reported the association between the policy stringency and total cases in E7 countries during the COVID-19 period. In this study, we also found that countries with the most disadvantaged communities, vulnerable health system, and less resources (including health system as well water and sanitation) were associated with more COVID-19 cases in BRICS countries.…”

“…In previous studies, a daily policy stringency index was used to reflect the level of policy response [6,[18][19][20]. The national stringency index, compiled from a global database of pandemic policies (Oxford COVID-19 Government Response Tracker, OxCGRT), addresses the need for continuously updated, readily usable and comparable information on policy measures [18].…”

Spatial-temporal variations in community mobility during lockdown, unlock, and the second wave of COVID-19 in India: A data-based analysis using Google's community mobility reports

“…pre-lockdown (30 January to 24 March 2020), lockdown (25 March to 31 May 2020), unlock (1 June 2020 to 10 February 2021), second-wave (11 February to 31 May 2021). The stringency index is a composite measure based on nine response indicators including school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100 (100 = strictest) ( Kayral and Buzrul, 2020 ; Royo, 2020 ). It provides an image of the phase at which any country imposed its strongest measures ( Sulyok and Walker, 2021 ).…”

Propuesta de un índice que resume las medidas preventivas no farmacológicas adoptadas frente al SARS-CoV-2 en las empresas, el caso del Parc de Salut Mar (Barcelona)

“…Este índice mide el nivel de restricción de las políticas de contención y de cierre aplicadas en cada país durante la pandemia, combinando nueve indicadores: cierre de escuelas, cierre de lugares de trabajo, cancelación de eventos públicos, restricciones a las reuniones, cierre del transporte público, campañas de información pública, confinamiento domiciliario, restricciones a los desplazamientos internos y controles de viajes internacionales. Este índice está siendo muy útil para evaluar la efectividad de las políticas públicas puestas en marcha por los gobiernos de los distintos países o territorios, permitiendo la comparación entre ellos (3) .…”