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“Know your epidemic, know your response”: a useful approach, if we get it right

Utilizing population-based data from the Covid-19 phone survey ($N=2,262$) of the Malawi Longitudinal Study of Families and Health (MLSFH) collected during June 2nd–August 17th, 2020, we focus on the crucial role that community leadership and trust in institutions played in shaping behavioral, economic and social responses to Covid-19 in this low-income sub-Saharan African context. We argue that the effective response of Malawi to limit the spread of the virus was facilitated by the engagement of local leadership to mobilize communities to adapt and adhere to Covid-19 prevention strategies. Using linear and ordered probit models and controlling for time fixed effects, we show that village heads (VHs) played pivotal role in shaping individuals’ knowledge about the pandemic and the adoption of preventive health behaviors and were crucial for mitigating the negative economic and health consequences of the pandemic. We further show that trust in institutions is of particular importance in shaping individuals’ behavior during the pandemic, and these findings highlight the pivotal role of community leadership in fostering better compliance and adoption of public health measures essential to contain the virus. Overall, our findings point to distinctive patterns of pandemic response in a low-income sub-Saharan African rural population that emphasized local leadership as mediators of public health messages and policies. These lessons from the first pandemic wave remain relevant as in many low-income countries behavioral responses to Covid-19 will remain the primary prevention strategy for a foreseeable future.

Public health messages shape how the world understands the HIV epidemic. Considerable inequalities remain in HIV care continuum indicators by subpopulation and geography (eg, highest infection and mortality burden among men who have sex with men and people who live in sub-Saharan Africa). Health equity-focused approaches are necessary in this next decade to close gaps in the HIV epidemic. Between 1981 and 1989, HIV messages triggered fear and victim blaming, and highlighted behaviours of a few marginalised groups as deviant. Between 1990 and 1999, messages signalled that HIV was a growing challenge for the world and required multisector approaches that addressed structural drivers of inequality. Between 2000 and 2009, messages highlighted universal testing, while advances in HIV testing made these messages easier for individuals to respond to than in previous decades. Currently, messages signal that ending HIV is possible, people can live productive lives with HIV, and transmission to people without HIV can be eliminated. Public health messaging about the HIV epidemic has evolved substantially over the past 40 years. Future HIV messaging should be driven by health equity principles that include an increased representation of key populations in message design and dissemination, transparency of funding, and communicating any impact that campaigns have had on closing health inequalities.

Generalized additive models (GAMs) were used to predict non-linear distributions of HIV prevalence and incidence based on semiparametric methods. The GAMs also provide smooth intensity maps by projecting the predicted HIV prevalence (or incidence) into the contour maps. Two sets of geo-coded data sources were used: (1) population-based cross-sectional data from 10,928 women who participated in four HIV behavioral surveys (2002–2017), (2) clinic-based longitudinal data from 7,557 women who resided in KwaZulu-Natal (2002–2016). Model estimated degrees of freedoms were 15.84,12.17,7.64 and 15.08 (2002-2012), indicating substantial spatial variations in HIV prevalence overtime. At localized-level these HIV incidence ranged from 15 to 18 per 100 person-year and scattered across the relatively homogeneous area within less than 100 km radius. These significant quantitative evidence were further supported by continuous-scale intensity maps. Our findings provided empirical and visual evidence for the changing face of the epidemic in South Africa using geospatial methods.

The role of geospatial disparities in the dynamics of the COVID-19 pandemic is poorly understood. We developed a spatially-explicit mathematical model to simulate transmission dynamics of COVID-19 disease infection in relation with the uneven distribution of the healthcare capacity in Ohio, U.S. The results showed substantial spatial variation in the spread of the disease, with localized areas showing marked differences in disease attack rates. Higher COVID-19 attack rates experienced in some highly connected and urbanized areas (274 cases per 100,000 people) could substantially impact the critical health care response of these areas regardless of their potentially high healthcare capacity compared to more rural and less connected counterparts (85 cases per 100,000). Accounting for the spatially uneven disease diffusion linked to the geographical distribution of the critical care resources is essential in designing effective prevention and control programmes aimed at reducing the impact of COVID-19 pandemic.