U.S. county-level characteristics to inform equitable COVID-19 response.

Background: The spread of Coronavirus Disease 2019 (COVID-19) across the United States confirms that not all Americans are equally at risk of infection, severe disease, or mortality. A range of intersecting biological, demographic, and socioeconomic factors are likely to determine an individual's susceptibility to COVID-19. These factors vary significantly across counties in the United States, and often reflect the structural inequities in our society. Recognizing this vast inter-county variation in risks will be critical to mounting an adequate response strategy. Methods and Findings: Using publicly available county-specific data we identified key biological, demographic, and socioeconomic factors influencing susceptibility to COVID-19, guided by international experiences and consideration of epidemiological parameters of importance. We created bivariate county-level maps to summarize examples of key relationships across these categories, grouping age and poverty; comorbidities and lack of health insurance; proximity, density and bed capacity; and race and ethnicity, and premature death. We have also made available an interactive online tool that allows public health officials to query risk factors most relevant to their local context.Our data demonstrate significant inter-county variation in key epidemiological risk factors, with a clustering of counties in certain states, which will result in an increased demand on their public health system. While the East and West coast cities are particularly vulnerable owing to their densities (and travel routes), a large number of counties in the Southeastern states have a high proportion of at-risk populations, with high levels of poverty, comorbidities, and premature death at baseline, and low levels of health insurance coverage.The list of variables we have examined is by no means comprehensive, and several of them are interrelated and magnify underlying vulnerabilities. The online tool allows readers to explore additional combinations of risk factors, set categorical thresholds for each covariate, and filter counties above different population thresholds. Conclusion: COVID-19 responses and decision making in the United States remain decentralized. Both the federal and state governments will benefit from recognizing high intra-state, inter-county variation in population risks and response capacity. Many of the factors that are likely to exacerbate the burden of COVID-19 and the demand on healthcare systems are the compounded result of long-standing structural inequalities in US society. Strategies to protect those in the most vulnerable counties will require urgent measures to better support communities' attempts at social distancing and to accelerate cooperation across jurisdictions to supply personnel and equipment to counties that will experience high demand.

Individual quarantine versus active monitoring of contacts for the mitigation of COVID-19: a modelling study.

BACKGROUND: Voluntary individual quarantine and voluntary active monitoring of contacts are core disease control strategies for emerging infectious diseases such as COVID-19. Given the impact of quarantine on resources and individual liberty, it is vital to assess under what conditions individual quarantine can more effectively control COVID-19 than active monitoring. As an epidemic grows, it is also important to consider when these interventions are no longer feasible and broader mitigation measures must be implemented. METHODS: To estimate the comparative efficacy of individual quarantine and active monitoring of contacts to control severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we fit a stochastic branching model to reported parameters for the dynamics of the disease. Specifically, we fit a model to the incubation period distribution (mean 5·2 days) and to two estimates of the serial interval distribution: a shorter one with a mean serial interval of 4·8 days and a longer one with a mean of 7·5 days. To assess variable resource settings, we considered two feasibility settings: a high-feasibility setting with 90% of contacts traced, a half-day average delay in tracing and symptom recognition, and 90% effective isolation; and a low-feasibility setting with 50% of contacts traced, a 2-day average delay, and 50% effective isolation. FINDINGS: Model fitting by sequential Monte Carlo resulted in a mean time of infectiousness onset before symptom onset of 0·77 days (95% CI -1·98 to 0·29) for the shorter serial interval, and for the longer serial interval it resulted in a mean time of infectiousness onset after symptom onset of 0·51 days (95% CI -0·77 to 1·50). Individual quarantine in high-feasibility settings, where at least 75% of infected contacts are individually quarantined, contains an outbreak of SARS-CoV-2 with a short serial interval (4·8 days) 84% of the time. However, in settings where the outbreak continues to grow (eg, low-feasibility settings), so too will the burden of the number of contacts traced for active monitoring or quarantine, particularly uninfected contacts (who never develop symptoms). When resources are prioritised for scalable interventions such as physical distancing, we show active monitoring or individual quarantine of high-risk contacts can contribute synergistically to mitigation efforts. Even under the shorter serial interval, if physical distancing reduces the reproductive number to 1·25, active monitoring of 50% of contacts can result in overall outbreak control (ie, effective reproductive number <1). INTERPRETATION: Our model highlights the urgent need for more data on the serial interval and the extent of presymptomatic transmission to make data-driven policy decisions regarding the cost-benefit comparisons of individual quarantine versus active monitoring of contacts. To the extent that these interventions can be implemented, they can help mitigate the spread of SARS-CoV-2. FUNDING: National Institute of General Medical Sciences, National Institutes of Health.

Rapid Forecasting of Cholera Risk in Mozambique: Translational Challenges and Opportunities.

Disasters, such as cyclones, create conditions that increase the risk of infectious disease outbreaks. Epidemic forecasts can be valuable for targeting highest risk populations before an outbreak. The two main barriers to routine use of real-time forecasts include scientific and operational challenges. First, accuracy may be limited by availability of data and the uncertainty associated with the inherently stochastic processes that determine when and where outbreaks happen and spread. Second, even if data are available, the appropriate channels of communication may prevent their use for decision making.In April 2019, only six weeks after Cyclone Idai devastated Mozambique's central region and sparked a cholera outbreak, Cyclone Kenneth severely damaged northern areas of the country. By June 10, a total of 267 cases of cholera were confirmed, sparking a vaccination campaign. Prior to Kenneth's landfall, a team of academic researchers, humanitarian responders, and health agencies developed a simple model to forecast areas at highest risk of a cholera outbreak. The model created risk indices for each district using combinations of four metrics: (1) flooding data; (2) previous annual cholera incidence; (3) sensitivity of previous outbreaks to the El Niño-Southern Oscillation cycle; and (4) a diffusion (gravity) model to simulate movement of infected travelers. As information on cases became available, the risk model was continuously updated. A web-based tool was produced, which identified highest risk populations prior to the cyclone and the districts at-risk following the start of the outbreak.The model prior to Kenneth's arrival using the metrics of previous incidence, projected flood, and El Niño sensitivity accurately predicted areas at highest risk for cholera. Despite this success, not all data were available at the scale at which the vaccination campaign took place, limiting the model's utility, and the extent to which the forecasts were used remains unclear. Here, the science behind these forecasts and the organizational structure of this collaborative effort are discussed. The barriers to the routine use of forecasts in crisis settings are highlighted, as well as the potential for flexible teams to rapidly produce actionable insights for decision making using simple modeling tools, both before and during an outbreak.

Challenges facing community health workers in Brazil's Family Health Strategy: A qualitative study.

Community health worker (CHW) programs are implemented in many low- and middle-income countries such as Brazil to increase access to and quality of care for underserved populations; CHW programs have been found to improve certain indicators of health, but few studies have investigated the daily work of CHWs, their perspectives on what both helps and hinders them from fulfilling their roles, and ways that their effectiveness and job satisfaction could be increased. To examine these questions, we observed clinic visits, CHW home visits, and conducted semistructured interviews with CHWs in 7 primary care centers in Brazil-2 in Salvador, Bahia, and 5 in São Paulo, SP-in which CHWs are incorporated into the work of all primary care health teams. In addition to enhancing communication between the medical system and the community, CHWs consider their key roles to be helping persuade community members to seek medical care and increasing health professionals' awareness of the social conditions affecting their patients' health. Key obstacles that CHWs face include failure to be fully integrated into the primary care team, inability to follow-up on identified health needs due to limited resources, as well as community members' lack of understanding of their work and undervaluing of preventative medicine. Increased training, better incorporation of CHWs into clinic flow and decision making, and establishing a clear community awareness of the roles and value of CHWs will help increase the motivation and effectiveness of CHWs in Brazil.

Prevalence of HIV and hepatitis B virus co-infection in sub-Saharan Africa and the potential impact and program feasibility of hepatitis B surface antigen screening in resource-limited settings.

BACKGROUND: Screening people living with HIV for hepatitis B virus (HBV) co-infection is recommended in resource-rich settings to optimize HIV antiretroviral therapy (ART) and mitigate HBV-related liver disease. This review examines the need, feasibility, and impact of screening for HBV in resource-limited settings (RLS). METHODS: We searched 6 databases to identify peer-reviewed publications between 2007 and 2013 addressing (1) HIV/HBV co-infection frequency in sub-Saharan Africa (SSA); (2) performance of hepatitis B surface antigen (HBsAg) rapid strip assays (RSAs) in RLS; (3) impact of HBV co-infection on morbidity, mortality, or liver disease progression; and/or (4) impact of HBV-suppressive antiretroviral medications as part of ART on at least one of 5 outcomes (mortality, morbidity, HIV transmission, retention in HIV care, or quality of life). We rated the quality of individual articles and summarized the body of evidence and expected impact of each intervention per outcome addressed. RESULTS: Of 3940 identified studies, 85 were included in the review: 55 addressed HIV/HBV co-infection frequency; 6 described HBsAg RSA performance; and 24 addressed the impact of HIV/HBV co-infection and ART. HIV/HBV frequency in sub-Saharan Africa varied from 0% to >28.4%. RSA performance in RLS showed good, although variable, sensitivity and specificity. Quality of studies ranged from strong to weak. Overall quality of evidence for the impact of HIV/HBV co-infection and ART on morbidity and mortality was fair and good to fair, respectively. CONCLUSIONS: Combined, the body of evidence reviewed suggests that HBsAg screening among people living with HIV could have substantial impact on preventing morbidity and mortality among HIV/HBV co-infected individuals in RLS.