Atypical causes of respiratory virus infections in Sub-Saharan Africa from 2013- 2023: a systematic review and meta-analysis

Abstract

Background: Atypical respiratory viruses (ARVs) are a diverse group of pathogens that cause respiratory infections through less common mechanisms or in unique epidemiological patterns, unlike the typical viruses like respiratory syncytial virus, influenza and human rhinoviruses. They sometimes present as unusual respiratory illnesses in vulnerable populations with near-fatal outcomes. Several viruses are involved, such as Human metapneumovirus (HMPV), Human Bocavirus (HBoV), Enteroviruses (EVs), Parechovirus (PeV) and Influenza C virus (ICV). This review was done to shed light on ARVs and their possible role in respiratory illness or infections based on studies in Sub-Saharan Africa from 2013 to 2023.

Methods: We systematically reviewed atypical causes of respiratory virus infections in Sub-Saharan Africa (SSA) in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines. We searched PubMed, Web of Science, Google Scholar and Cochrane Library to include studies published from 2013 to 2023 with reports on ARV. The protocol was registered in PROSPERO (ID: CRD42024611183).

Results: The review covered 46 SSA countries, with five eligible for the systematic review. The search yielded 548 publications, with only six studies meeting the inclusion criteria. Studies included children and individuals of all age groups. The prevalence of ARVs detected in SSA was as follows: HMPV pooled prevalence was 1.52% (95% CI: 1.07-2.00), EVs pooled prevalence was 15.0% (95% CI: 14.1-15.9), HBoV prevalence was 0.4%, PeV was 20%, and ICV was 1.3% in individuals with respiratory tract infection(s).

Conclusion: Our findings suggest testing or diagnostics for ARV infections are very low in SSA. Therefore, surveillance of people suffering from respiratory tract infections, which is lacking, needs to be improved to monitor the prevalence of ARVs and the role they play in respiratory disease outcomes.

Keywords: Atypical respiratory viruses; Diagnostic methods; Prevalence; Sub-Saharan Africa.

Fig. 1

Flow diagram of the study selection procedure * records included **records excluded

Fig. 2

The pooled prevalence of Human Metapneumovirus (HMPV)). The forest plot depicts the prevalence of HMPV as reported by individual studies [26, 28, 30, 31] [1st Year and 2nd Year], with corresponding 95% confidence intervals (blue lines). The pooled prevalence, calculated using a random-effects model, is represented by the red diamond, with the red dashed line indicating the pooled estimate. The x-axis shows the prevalence (proportion), while the y-axis lists the studies included

Fig. 3

Pooled prevalence of Enterovirus. The forest plot illustrates the prevalence of Enterovirus reported by individual studies [–28], and [30], with corresponding 95% confidence intervals (blue lines). The pooled prevalence, calculated using a random-effects model, is represented by the red diamond, while the red dashed line marks the pooled estimate. The x-axis displays the prevalence (proportion), and the y-axis lists the studies included in the analysis

Fig. 4

Funnel plot assessing publication bias in a meta-analysis of HMPV prevalence. The x-axis represents the effect size, while the y-axis represents the standard error. Open circles indicate individual studies, and shaded areas represent confidence regions. The symmetrical distribution of studies around the central axis suggests minimal publication bias, with smaller studies (higher standard error) concentrated at the top and larger studies (lower standard error) spread at the base. The presence of studies across varying standard error levels supports the methodological robustness and validity of the meta-analysis findings

Fig. 5

Funnel plot assessing publication bias in a meta-analysis of Enterovirus (EV) prevalence. Open circles indicate individual studies, with different shapes and colors representing specific studies. The shaded regions denote confidence intervals, with darker shades indicating greater certainty. The symmetrical distribution of studies around the central axis suggests minimal publication bias, with smaller studies (higher standard error) clustered at the top and larger studies (lower standard error) spread at the base. The presence of studies across different standard error levels supports the methodological robustness and validity of the meta-analysis findings