The Heterogeneous Severity of COVID-19 in African Countries: A Modeling Approach

Estimation of epidemiological parameters and ascertainment rate from early transmission of COVID-19 across Africa

Country reported case counts suggested a slow spread of SARS-CoV-2 in the initial phase of the COVID-19 pandemic in Africa. Owing to inadequate public awareness, unestablished monitoring practices, limited testing and stigmas, there might exist extensive under-ascertainment of the true number of cases, especially at the beginning of the novel epidemic. We developed a compartmentalized epidemiological model to track the early epidemics in 54 African countries. Data on the reported cumulative number of cases and daily confirmed cases were used to fit the model for the time period with no or little massive national interventions yet in each country. We estimated that the mean basic reproduction number is 2.02 (s.d. 0.7), with a range between 1.12 (Zambia) and 3.64 (Nigeria). The mean overall report rate was estimated to be 5.37% (s.d. 5.71%), with the highest 30.41% in Libya and the lowest 0.02% in São Tomé and Príncipe. An average of 5.46% (s.d. 6.4%) of all infected cases were severe cases and 66.74% (s.d. 17.28%) were asymptomatic ones. The estimated low reporting rates in Africa suggested a clear need for improved reporting and surveillance systems in these countries.

COVID-19 impacts on healthcare access in sub-Saharan Africa: an overview

This overview aimed to describe the situation of healthcare access in sub-Saharan Africa, excluding South Africa, during the COVID-19 pandemic. A PubMed^® search from March 31, 2020, to August 15, 2022, selected 116 articles. Healthcare access and consequences of COVID-19 were assessed based on comparisons with months before its onset or an identical season in previous years. A general reduction of healthcare delivery, associated with the decline of care quality, and closure of many specialty services were reported. The impact was heterogeneous in space and time, with an increase in urban areas at the beginning of the pandemic (March-June 2020). The return to normalcy was gradual from the 3^rd quarter of 2020 until the end of 2021. The impact of COVID-19 on the health system and its use was attributed to (a) conjunctural factors resulting from government actions to mitigate the spread of the epidemic (containment, transportation restrictions, closures of businesses, and places of entertainment or worship); (b) structural factors related to the disruption of public and private facilities and institutions, in particular, the health system; and (c) individual factors linked to the increase in costs, impoverishment of the population, and fear of contamination or stigmatization, which discouraged patients from going to health centers. They have caused considerable socio-economic damage. Several studies emphasized some adaptability of the healthcare offer and resilience of the healthcare system, despite its unpreparedness, which explained a return to normal activities as early as 2022 while the COVID-19 epidemic persisted. There appears to be a strong disproportion between the moderate incidence and severity of COVID-19 in sub-Saharan Africa, and the dramatic impact on healthcare access. Several articles make recommendations for lowering the socioeconomic consequences of future epidemics to ensure better management of health issues.

Assessing the Impact of Vaccination on the Dynamics of COVID-19 in Africa: A Mathematical Modeling Study

Several effective COVID-19 vaccines are administered to combat the COVID-19 pandemic globally. In most African countries, there is a comparatively limited deployment of vaccination programs. In this work, we develop a mathematical compartmental model to assess the impact of vaccination programs on curtailing the burden of COVID-19 in eight African countries considering SARS-CoV-2 cumulative case data for each country for the third wave of the COVID-19 pandemic. The model stratifies the total population into two subgroups based on individual vaccination status. We use the detection and death rates ratios between vaccinated and unvaccinated individuals to quantify the vaccine's effectiveness in reducing new COVID-19 infections and death, respectively. Additionally, we perform a numerical sensitivity analysis to assess the combined impact of vaccination and reduction in the SARS-CoV-2 transmission due to control measures on the control reproduction number (Rc). Our results reveal that on average, at least 60% of the population in each considered African country should be vaccinated to curtail the pandemic (lower the Rc below one). Moreover, lower values of Rc are possible even when there is a low (10%) or moderate (30%) reduction in the SARS-CoV-2 transmission rate due to NPIs. Combining vaccination programs with various levels of reduction in the transmission rate due to NPI aids in curtailing the pandemic. Additionally, this study shows that vaccination significantly reduces the severity of the disease and death rates despite low efficacy against COVID-19 infections. The African governments need to design vaccination strategies that increase vaccine uptake, such as an incentive-based approach.

A multi-strain model with asymptomatic transmission: Application to COVID-19 in the US

COVID-19, induced by the SARS-CoV-2 infection, has caused an unprecedented pandemic in the world. New variants of the virus have emerged and dominated the virus population. In this paper, we develop a multi-strain model with asymptomatic transmission to study how the asymptomatic or pre-symptomatic infection influences the transmission between different strains and control strategies that aim to mitigate the pandemic. Both analytical and numerical results reveal that the competitive exclusion principle still holds for the model with the asymptomatic transmission. By fitting the model to the COVID-19 case and viral variant data in the US, we show that the omicron variants are more transmissible but less fatal than the previously circulating variants. The basic reproduction number for the omicron variants is estimated to be 11.15, larger than that for the previous variants. Using mask mandate as an example of non-pharmaceutical interventions, we show that implementing it before the prevalence peak can significantly lower and postpone the peak. The time of lifting the mask mandate can affect the emergence and frequency of subsequent waves. Lifting before the peak will result in an earlier and much higher subsequent wave. Caution should also be taken to lift the restriction when a large portion of the population remains susceptible. The methods and results obtained her e may be applied to the study of the dynamics of other infectious diseases with asymptomatic transmission using other control measures.

Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling

Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.

[Impact of COVID-19 on public health in sub-Saharan Africa]

Objective

This work aimed to assess the impact of COVID-19 on healthcare supply in sub-Saharan Africa except South Africa.

Method

A search through PubMed® between April 2020 and August 2022 selected 135 articles. The impact of COVID-19 was assessed on comparisons with the months prior to the onset of COVID-19 or an identical season in previous years.

Results

The decline of health services, associated with a reduction in their quality, and the closure of specialized health units have been reported. Many control programs and public health interventions have been interrupted, with the risk of an increase of the corresponding diseases. Social disorganization has generated mental health issues among the population, including health personnel. The impact was heterogeneous in space and time. The main causes were attributed to containment measures (transport restrictions, trade closures) and the lack of human and material resources. The increase in costs, in addition to the impoverishment of the population, and the fear of being contaminated or stigmatized have discouraged patients from going to health centres. The studies mention the gradual return to normal after the first epidemic wave and the resilience of the healthcare system.

Conclusion

Several articles make recommendations aimed at reducing the impact of future epidemics: support for community workers, training of health workers and reorganization of services to improve the reception and care of patients, technological innovations (use of telephones, drones, etc.) and better information monitoring.

Effects of vaccination on mitigating COVID-19 outbreaks: a conceptual modeling approach

This paper is devoted to investigating the impact of vaccination on mitigating COVID-19 outbreaks. In this work, we propose a compartmental epidemic ordinary differential equation model, which extends the previous so-called SEIRD model ^[1,2,3,4] by incorporating the birth and death of the population, disease-induced mortality and waning immunity, and adding a vaccinated compartment to account for vaccination. Firstly, we perform a mathematical analysis for this model in a special case where the disease transmission is homogeneous and vaccination program is periodic in time. In particular, we define the basic reproduction number $ \mathcal{R}_0 $ for this system and establish a threshold type of result on the global dynamics in terms of $ \mathcal{R}_0 $. Secondly, we fit our model into multiple COVID-19 waves in four locations including Hong Kong, Singapore, Japan, and South Korea and then forecast the trend of COVID-19 by the end of 2022. Finally, we study the effects of vaccination again the ongoing pandemic by numerically computing the basic reproduction number $ \mathcal{R}_0 $ under different vaccination programs. Our findings indicate that the fourth dose among the high-risk group is likely needed by the end of the year.

A Multiscale Model of COVID-19 Dynamics

COVID-19, caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a global pandemic and created unprecedented public health challenges throughout the world. Despite significant progresses in understanding the disease pathogenesis and progression, the epidemiological triad of pathogen, host, and environment remains unclear. In this paper, we develop a multiscale model to study the coupled within-host and between-host dynamics of COVID-19. The model includes multiple transmission routes (both human-to-human and environment-to-human) and connects multiple scales (both the population and individual levels). A detailed analysis on the local and global dynamics of the fast system, slow system and full system shows that rich dynamics, including both forward and backward bifurcations, emerge with the coupling of viral infection and epidemiological models. Model fitting to both virological and epidemiological data facilitates the evaluation of the influence of a few infection characteristics and antiviral treatment on the spread of the disease. Our work underlines the potential role that the environment can play in the transmission of COVID-19. Antiviral treatment of infected individuals can delay but cannot prevent the emergence of disease outbreaks. These results highlight the implementation of comprehensive intervention measures such as social distancing and wearing masks that aim to stop airborne transmission, combined with surface disinfection and hand hygiene that can prevent environmental transmission. The model also provides a multiscale modeling framework to study other infectious diseases when the environment can serve as a reservoir of pathogens.

A simple model to estimate the transmissibility of the Beta, Delta, and Omicron variants of SARS-COV-2 in South Africa

The COVID-19 pandemic caused multiple waves of mortality in South Africa, where three genetic variants of SARS-COV-2 and their ancestral strain dominated consecutively. State-of-the-art mathematical modeling approach was used to estimate the time-varying transmissibility of SARS-COV-2 and the relative transmissibility of Beta, Delta, and Omicron variants. The transmissibility of the three variants were about 73%, 87%, and 276% higher than their preceding variants. To the best of our knowledge, our model is the first simple model that can simulate multiple mortality waves and three variants' replacements in South Africa. The transmissibility of the Omicron variant is substantially higher than that of previous variants.

A vaccination model for COVID-19 in Gauteng, South Africa

The COVID-19 pandemic provides an opportunity to explore the impact of government mandates on movement restrictions and non-pharmaceutical interventions on a novel infection, and we investigate these strategies in early-stage outbreak dynamics. The rate of disease spread in South Africa varied over time as individuals changed behavior in response to the ongoing pandemic and to changing government policies. Using a system of ordinary differential equations, we model the outbreak in the province of Gauteng, assuming that several parameters vary over time. Analyzing data from the time period before vaccination gives the approximate dates of parameter changes, and those dates are linked to government policies. Unknown parameters are then estimated from available case data and used to assess the impact of each policy. Looking forward in time, possible scenarios give projections involving the implementation of two different vaccines at varying times. Our results quantify the impact of different government policies and demonstrate how vaccinations can alter infection spread.

The basic reproduction number of COVID-19 across Africa

The pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) took the world by surprise. Following the first outbreak of COVID-19 in December 2019, several models have been developed to study and understand its transmission dynamics. Although the spread of COVID-19 is being slowed down by vaccination and other interventions, there is still a need to have a clear understanding of the evolution of the pandemic across countries, states and communities. To this end, there is a need to have a clearer picture of the initial spread of the disease in different regions. In this project, we used a simple SEIR model and a Bayesian inference framework to estimate the basic reproduction number of COVID-19 across Africa. Our estimates vary between 1.98 (Sudan) and 9.66 (Mauritius), with a median of 3.67 (90% CrI: 3.31-4.12). The estimates provided in this paper will help to inform COVID-19 modeling in the respective countries/regions.

Analysis of a COVID-19 Epidemic Model with Seasonality

The statistics of COVID-19 cases exhibits seasonal fluctuations in many countries. In this paper, we propose a COVID-19 epidemic model with seasonality and define the basic reproduction number [Formula: see text] for the disease transmission. It is proved that the disease-free equilibrium is globally asymptotically stable when [Formula: see text], while the disease is uniformly persistent and there exists at least one positive periodic solution when [Formula: see text]. Numerically, we observe that there is a globally asymptotically stable positive periodic solution in the case of [Formula: see text]. Further, we conduct a case study of the COVID-19 transmission in the USA by using statistical data.