tetano
Editor, Senior Moderator
R Soc Open Sci
. 2025 Jan 22;12(1):241275.
doi: 10.1098/rsos.241275. eCollection 2025 Jan. SARS-CoV-2 transmission dynamics in Mozambique and Zimbabwe during the first 3 years of the pandemic
Roselyn F Kaondera-Shava[SUP] 1 [/SUP], Marta Galanti[SUP] 1 [/SUP], Matteo Perini[SUP] 1 [/SUP], Jiyeon Suh[SUP] 1 [/SUP], Shannon M Farley[SUP] 2 [/SUP], Sergio Chicumbe[SUP] 3 [/SUP], Ilesh Jani[SUP] 3 [/SUP], Annette Cassy[SUP] 3 [/SUP], Ivalda Macicame[SUP] 3 [/SUP], Naisa Manafe[SUP] 3 [/SUP], Wafaa El-Sadr[SUP] 4 [/SUP], Jeffrey Shaman[SUP] 1 5 [/SUP]
Affiliations
The 2019 emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its rapid spread created a public health emergency of international concern. However, the impact of the pandemic in Sub-Saharan Africa, as documented in cases, hospitalizations and deaths, appears far lower than in the Americas, Europe and Asia. Characterization of the transmission dynamics is critical for understanding how SARS-CoV-2 spreads and the true scale of the pandemic. Here, to better understand SARS-CoV-2 transmission dynamics in two southern African countries, Mozambique and Zimbabwe, we developed a dynamic model-Bayesian inference system to estimate key epidemiological parameters, namely the transmission and ascertainment rates. Total infection burdens (reported and unreported) during the first 3 years of the pandemic were reconstructed using a model-inference approach. Transmission rates rose with each successive wave, which aligns with observations in other continents. Ascertainment rates were found to be low and consistent with other African countries. Overall, the estimated disease burden was higher than the documented cases, indicating a need for improved reporting and surveillance. These findings aid understanding of COVID-19 disease and respiratory virus transmission dynamics in two African countries little investigated to date and can help guide future public health planning and control strategies.
Keywords: Bayesian inference; COVID-19; SARS-CoV-2; ascertainment rate; dynamic model; transmission rate.
. 2025 Jan 22;12(1):241275.
doi: 10.1098/rsos.241275. eCollection 2025 Jan. SARS-CoV-2 transmission dynamics in Mozambique and Zimbabwe during the first 3 years of the pandemic
Roselyn F Kaondera-Shava[SUP] 1 [/SUP], Marta Galanti[SUP] 1 [/SUP], Matteo Perini[SUP] 1 [/SUP], Jiyeon Suh[SUP] 1 [/SUP], Shannon M Farley[SUP] 2 [/SUP], Sergio Chicumbe[SUP] 3 [/SUP], Ilesh Jani[SUP] 3 [/SUP], Annette Cassy[SUP] 3 [/SUP], Ivalda Macicame[SUP] 3 [/SUP], Naisa Manafe[SUP] 3 [/SUP], Wafaa El-Sadr[SUP] 4 [/SUP], Jeffrey Shaman[SUP] 1 5 [/SUP]
Affiliations
- PMID: 39845719
- PMCID: PMC11750399
- DOI: 10.1098/rsos.241275
The 2019 emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its rapid spread created a public health emergency of international concern. However, the impact of the pandemic in Sub-Saharan Africa, as documented in cases, hospitalizations and deaths, appears far lower than in the Americas, Europe and Asia. Characterization of the transmission dynamics is critical for understanding how SARS-CoV-2 spreads and the true scale of the pandemic. Here, to better understand SARS-CoV-2 transmission dynamics in two southern African countries, Mozambique and Zimbabwe, we developed a dynamic model-Bayesian inference system to estimate key epidemiological parameters, namely the transmission and ascertainment rates. Total infection burdens (reported and unreported) during the first 3 years of the pandemic were reconstructed using a model-inference approach. Transmission rates rose with each successive wave, which aligns with observations in other continents. Ascertainment rates were found to be low and consistent with other African countries. Overall, the estimated disease burden was higher than the documented cases, indicating a need for improved reporting and surveillance. These findings aid understanding of COVID-19 disease and respiratory virus transmission dynamics in two African countries little investigated to date and can help guide future public health planning and control strategies.
Keywords: Bayesian inference; COVID-19; SARS-CoV-2; ascertainment rate; dynamic model; transmission rate.