Comparing COVID-19 physical distancing policies: results from a physical distancing intensity coding framework for Botswana, India, Jamaica, Mozambique, Namibia, Ukraine, and the United States

Abstract

Background Understanding the differences in timing and composition of physical distancing policies is important to evaluate the early global response to COVID-19. A physical distancing intensity monitoring framework comprising 16 domains was recently published to compare physical distancing approaches across 12 U.S. States. We applied this framework to a diverse set of low and middle-income countries (LMICs) (Botswana, India, Jamaica, Mozambique, Namibia, and Ukraine) to test the appropriateness of this framework in the global context and to compare the policy responses in these LMICs with a sample of U.S. States during the first 100-days of the pandemic. Results The LMICs in our sample adopted wide ranging physical distancing policies. The highest peak daily physical distancing intensity during this period was: Botswana (4.60); India (4.40); Ukraine (4.40); Namibia (4.20); Mozambique (3.87), and Jamaica (3.80). The number of days each country stayed at peak policy intensity ranged from 12-days (Jamaica) to more than 67-days (Mozambique). We found some key similarities and differences, including substantial differences in whether and how countries expressly required certain groups to stay at home. Despite the much higher number of cases in the US, the physical distancing responses in our LMIC sample were generally more intense than in the U.S. States, but results vary depending on the U.S. State. The peak policy intensity for the U.S. 12-state average was 3.84, which would place it lower than every LMIC in this sample except Jamaica. The LMIC sample countries also reached peak physical distancing intensity earlier in outbreak progression compared to the U.S. states sample. The easing of physical distancing policies in the LMIC sample did not discernably correlate with change in COVID-19 incidence. Conclusions This physical distancing intensity framework was appropriate for the LMIC context with only minor adaptations. This framework may be useful for ongoing monitoring of physical distancing policy approaches and for use in effectiveness analyses. This analysis helps to highlight the differing paths taken by the countries in this sample and may provide lessons to other countries regarding options for structuring physical distancing policies in response to COVID-19 and future outbreaks.

Informed Random Forest to Model Associations of Epidemiological Priors, Government Policies, and Public Mobility

“…Such hypotheses are also made for populated countries such as Brazil, China, India, and the United States 53 as well as for low- and middle-income countries (Botswana, India, Jamaica, Mozambique, Namibia, and Ukraine). 54 This work extends the study by Lane et al 54 by exploring analyses of up to 906 (they used 100) days from the onset of COVID-19.…”

The significance of feeling safe for resilience of adolescents in sub-Saharan Africa

“…Data were collected over the period of March and October 2020. By March 2020, since the World Health Organization (WHO) had declared COVID-19 a “Public Health Emergency of International Concern” on 30 January 2020 and a “pandemic” (p. 2) on 11 March 2020 ( Lane et al, 2021 , p. 2), all the countries considered in the current study already had introduced some policies and containment measures during (or before) the month of March 2020; details and timelines can be found at United Nations [UN] (2022) for Angolo, Nchanji and Lutomia (2021) for Eswatini, Lesotho, and Kenya, Lane et al (2021) for Mozambique and Namibia, and Haider et al (2020) for South Africa, Uganda and Zambia. As all countries were aware of the global public health emergency status and had implemented some level of response measures to address the pandemic’s impact by the time of data collection, although we cannot measure how the COVID-19 pandemic restrictions may have impacted the adolescents’ responses, the adolescents’ experiences were captured in all nine counties under the shared knowledge and awareness of COVID-19’s severity and implications.…”

Characterizing social contact patterns in rural and urban Mozambique: the GlobalMix study, 2021-2022

“…Despite this, we had a good buy in from the community due to our extensive community engagement conducted before and during the study, which also resulted in higher participation rates in general than previously observed in other sub-Saharan African settings. Furthermore, the data was collected during periods of active transmission of SARS-CoV-2 in the community, including certain times when certain restrictive measures were in place, such as the closure of schools and parks, limitations on the number of employees at the workplace and passengers on public transportation, social distancing, and other measures [23]. This context likely caused some disruption, given the contingency scenario at the time, and may have influenced the characteristics of social contact patterns due to the ongoing pandemic.…”