Introduction

few studies have assessed risk for coronavirus disease 2019 (COVID-19) within African countries. Here we examine differences in vulnerability to COVID-19 among the ten administrative regions and two major cities of Cameroon based on epidemiological risk factors and access to healthcare resources.

Methods

regional epidemiological and healthcare access vulnerability indices were created and compared with cumulative COVID-19 cases, case fatality rates, co-morbidities, and healthcare resources in Cameroon.

Results

based on epidemiological risk factors, populations in the East Region, Douala (in the Littoral Region), West Region, and Yaoundé (in the Center Region) are at highest risk for COVID-19. Meanwhile, the North, Far North, East, and Adamawa Regions had the most healthcare access vulnerability. COVID-19 cases per population were highest in the Center, Littoral, and East Regions. Case fatality rates were greatest in the North Region. Potential co-morbidities with greater prevalence among COVID-19 patients included male sex, hypertension, and diabetes.

Conclusion

epidemiological risk factors for COVID-19 and access to healthcare varies between the regions of Cameroon. These discrepancies are potentially reflected in regional differences of COVID-19 cases and case fatality rates. In particular, the East Region has high epidemiological risk factors and low healthcare accessibility compared to other regions. Understanding the relationships between epidemiological risk factors, access to healthcare resources, and COVID-19 cases in Cameroon could aid decision-making among national policymakers and inform further research.

Globally, countries have used diverse methods to report data during the COVID-19 pandemic. Using international guidelines and principles of emergency management, we compare national data reporting systems in African countries in order to determine lessons for future pandemics. We analyse COVID-19 reporting practices across 54 African countries through 2020. Reporting systems were diverse and included summaries, press releases, situation reports and online dashboards. These systems were communicated via social media accounts and websites belonging to ministries of health and public health. Data variables from the reports included event detection (cases/deaths/recoveries), risk assessment (demographics/co-morbidities) and response (total tests/hospitalisations). Of countries with reporting systems, 36/53 (67.9%) had recurrent situation reports and/or online dashboards which provided more extensive data. All of these systems reported cases, deaths and recoveries. However, few systems contained risk assessment and response data, with only 5/36 (13.9%) reporting patient co-morbidities and 9/36 (25%) including total hospitalisations. Further evaluation of reporting practices in Cameroon, Egypt, Kenya, Senegal and South Africa as examples from different sub-regions revealed differences in reporting healthcare capacity and preparedness data. Improving the standardisation and accessibility of national data reporting systems could augment research and decision-making, as well as increase public awareness and transparency for national governments.

HIV-1 is an immunosuppressive pathogen. Our behavioral data for 191 HIV-1-infected rural Cameroonians show frequent exposure to nonhuman primates through activities such as hunting and butchering. Immunosuppression among persons exposed to body fluids of wild nonhuman primates could favor the process of adaptation and subsequent emergence of zoonotic pathogens.

Recent outbreaks of Ebola virus disease and Zika virus disease highlight the need for disseminating accurate predictions of emerging zoonotic viruses to national governments for disease surveillance and response. Although there are published maps for many emerging zoonotic viruses, it is unknown if there is agreement among different models or if they are concordant with national expert opinion. Therefore, we reviewed existing predictions for five high priority emerging zoonotic viruses with national experts in Cameroon to investigate these issues and determine how to make predictions more useful for national policymakers. Predictive maps relied primarily on environmental parameters and species distribution models. Rift Valley fever virus and Crimean-Congo hemorrhagic fever virus predictions differed from national expert opinion, potentially because of local livestock movements. Our findings reveal that involving national experts could elicit additional data to improve predictions of emerging pathogens as well as help repackage predictions for policymakers.