Distribution of the initial effective reproduction number (<i>R_t</i>) across 100 simulations for the pandemic influenza A(H1N1)pdm09 epidemic in South Africa, assuming known serial interval (SI) estimates derived from (A) confirmed secondary cases only (SI: 2.3 days) and (B) confirmed plus suspected secondary cases (SI: 2.7 days) in the transmission chain (method 2).

<p>Distribution of the initial effective reproduction number (<i>R_t</i>) across 100 simulations for the pandemic influenza A(H1N1)pdm09 epidemic in South Africa, assuming known serial interval (SI) estimates derived from (A) confirmed secondary cases only (SI: 2.3 days) and (B) confirmed plus suspected secondary cases (SI: 2.7 days) in the transmission chain (method 2).</p

Distribution of serial interval and initial effective reproductive number (<i>R_t</i>) across 100 simulations for the influenza A(H1N1)pdm09 epidemic in South Africa using the likelihood-based simultaneous estimation method (method 1).

<p>Distribution of serial interval and initial effective reproductive number (<i>R_t</i>) across 100 simulations for the influenza A(H1N1)pdm09 epidemic in South Africa using the likelihood-based simultaneous estimation method (method 1).</p

Temporal variation in the mean effective reproductive number () of influenza A(H1N1)pdm09 in South Africa, June 15 to October 4, 2009 (method 3).

<p>Temporal variation in the mean effective reproductive number () of influenza A(H1N1)pdm09 in South Africa, June 15 to October 4, 2009 (method 3).</p

Observed lag-time between date of symptom onset and date of specimen collection, incidence rate ratio (IRR) and significance value of the covariates significant in the Poisson regression model.

a<p>Pooled p-value for province covariate.</p

Epidemic curve of laboratory-confirmed influenza A(H1N1)pdm09 cases, South Africa, June 12 to September 30, 2009.

<p>Bars show original recorded data applying date of symptom onset where available (n = 758) and substitute by date of specimen collection where onset was unavailable (total n = 12,526). The line shows imputed data where date of symptom onset for missing case-based data was obtained by multiple imputations adjusted by provincial location of specimen collection and the occurrence of a case on a weekend day (n = 12,491).</p

Potential Impact of Co-Infections and Co-Morbidities Prevalent in Africa on Influenza Severity and Frequency: A Systematic Review

<div><p>Infectious diseases and underlying medical conditions common to Africa may affect influenza frequency and severity. We conducted a systematic review of published studies on influenza and the following co-infections or co-morbidities that are prevalent in Africa: dengue, malaria, measles, meningococcus, <i>Pneumocystis jirovecii</i> pneumonia (PCP), hemoglobinopathies, and malnutrition. Articles were identified except for influenza and PCP. Very few studies were from Africa. Sickle cell disease, dengue, and measles co-infection were found to increase the severity of influenza disease, though this is based on few studies of dengue and measles and the measles study was of low quality. The frequency of influenza was increased among patients with sickle cell disease. Influenza infection increased the frequency of meningococcal disease. Studies on malaria and malnutrition found mixed results. Age-adjusted morbidity and mortality from influenza may be more common in Africa because infections and diseases common in the region lead to more severe outcomes and increase the influenza burden. However, gaps exist in our knowledge about these interactions.</p></div

PRISMA flow diagram for systematic reviews of influenza and dengue (D), malaria (MR), measles (MS), meningococcus (MN), <i>Pneumocystis jirovecii</i> pneumonia (PCP), hemoglobinopathies (H), and malnutrition (MT).

<p>PRISMA flow diagram for systematic reviews of influenza and dengue (D), malaria (MR), measles (MS), meningococcus (MN), <i>Pneumocystis jirovecii</i> pneumonia (PCP), hemoglobinopathies (H), and malnutrition (MT).</p

R estimates for each data endpoint, based on national daily time series of rt-PCR-confirmed cases, hospitalisations, and deaths.

R estimates for each data endpoint (upper panel), South Africa, based on (lower panel) national daily time series of rt-PCR-confirmed cases, hospitalisations, and deaths. R estimated using 7-day sliding windows, from early March 2020 through 25 April. Results reflect median values (between imputations) of median R estimates and associated 2.5% and 97.5% credible intervals. L = Level. Red-shaded areas indicate the period during which civil unrest caused severe disruptions to surveillance in KwaZulu-Natal and Gauteng provinces; grey-shaded areas indicate gradually diminishing effects on R estimates.</p

Additional results and analyses.

ObjectivesThe aim of this study was to quantify transmission trends in South Africa during the first four waves of the COVID-19 pandemic using estimates of the time-varying reproduction number (R) and to compare the robustness of R estimates based on three different data sources, and using data from public and private sector service providers.MethodsR was estimated from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalisations, and hospital-associated deaths, using a method that models daily incidence as a weighted sum of past incidence, as implemented in the R package EpiEstim. R was also estimated separately using public and private sector data.ResultsNationally, the maximum case-based R following the introduction of lockdown measures was 1.55 (CI: 1.43–1.66), 1.56 (CI: 1.47–1.64), 1.46 (CI: 1.38–1.53) and 3.33 (CI: 2.84–3.97) during the first (Wuhan-Hu), second (Beta), third (Delta), and fourth (Omicron) waves, respectively. Estimates based on the three data sources (cases, hospitalisations, deaths) were generally similar during the first three waves, but higher during the fourth wave for case-based estimates. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave.ConclusionAgreement between R estimates using different data sources during the first three waves suggests that data from any of these sources could be used in the early stages of a future pandemic. The high R estimates for Omicron relative to earlier waves are interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights that clients of the public and private sectors did not experience two separate epidemics, except perhaps to a limited extent during the strictest lockdowns in the first wave.</div

R estimates by sector, based on rt-PCR-confirmed COVID-19 cases, hospitalisations, and deaths.

R estimates by sector, based on rt-PCR-confirmed COVID-19 cases (upper panel), hospitalisations (middle panel), and deaths (lower panel), South Africa. R estimates were generated using 7-day sliding windows. Results reflect median values (between imputations) of median R estimates and associated 2.5% and 97.5% credible intervals. L = Level. Red-shaded areas indicate the period during which civil unrest caused severe disruptions to surveillance in KwaZulu-Natal and Gauteng provinces; grey-shaded areas indicate gradually diminishing effects on R estimates.</p