Does a One Health Approach to Human African Trypanosomiasis Control Hasten Elimination? A Stochastic Compartmental Modeling Approach

Background: In response to large strides in the control of human African trypanosomiasis (HAT), in the early 2000s the WHO set targets for elimination of both the gambiense (gHAT) and rhodesiense (rHAT) forms as a public health (EPHP) problem by 2020, and elimination of gHAT transmisson (EOT) by 2030. While global EPHP targets have been met, and EOT appears within reach, there is ample evidence that current control strategies will not achieve gHAT EOT in the presence of animal reservoirs, the role of which is currently uncertain. Furthermore, rHAT is not targeted for EOT due to the known importance of animal reservoirs for this form. Methods: To evaluate the utility of a One Health approach to gHAT and rHAT EOT, we built and parameterized a compartmental stochastic model, using the Institute for Disease Modeling's Compartmental Modeling Software, to six HAT epidemics: the national rHAT epidemics in Uganda and Malawi, the national gHAT epidemics in Uganda and South Sudan, and two separate gHAT epidemics in Democratic Republic of Congo distinguished by dominant vector species. In rHAT foci the reservoir animal sub-model was stratified on four species groups, while in gHAT foci domestic swine were assumed to be the only competent reservoir. The modeled time horizon was 2005-2045, with calibration performed using HAT surveillance data from 2000-2004 and Optuna. Interventions included insecticide and trypanocide treatment of domestic animal reservoirs at varying coverage levels. Results: Validation against HAT surveillance data indicates favorable performance overall, with the possible exception of DRC. EOT was not observed in any modeled scenarios for rHAT, however insecticide treatment consistently performed better than trypanocide treatment in terms of rHAT control. EOT was not observed for gHAT at 0% coverage of domestic reservoirs with trypanocides or insecticides, but was observed by 2030 in all test scenarios; again, insecticides demonstrated superior performance to trypanocides. Conclusions: EOT cannot be achieved for rHAT without control of wildlife reservoirs, however insecticide treatment of domestic animals holds promise for improved control. In the presence of domestic animal reservoirs, gHAT EOT will not be achieved under current control strategies.

Vaccine effectiveness against severe acute respiratory infections (SARI) COVID-19 hospitalisations estimated from real-world surveillance data, Slovenia, October 2021

We estimated vaccine effectiveness (VE) against severe COVID-19 during October 2021, using Slovenian surveillance data. For people fully vaccinated with any vaccine in age groups 18–49, 50–64, ≥ 65 years, VE was 86% (95% CI: 79–90), 89% (85–91), and 77% (74–81). Among ≥ 65 year-olds fully vaccinated with mRNA vaccines, VE decreased from 93% (95% CI: 88–96) in those vaccinated ≤ 3 months ago to 43% (95% CI: 30–54) in those vaccinated ≥ 6 months ago, suggesting the need for early boosters.

Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data

New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could facilitate outbreak control efforts and highlight new variants that warrant further study. Here we develop an analytical epidemiological model that infers the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that strongly affect the transmission rate, both within and outside the Spike protein. We also quantify the effects of travel and competition between different lineages on the inferred transmission effects of mutations. Importantly, our model detects lineages with increased transmission as they arise. We infer significant transmission advantages for the Alpha and Delta variants within a week of their appearances in regional data, when their regional frequencies were only around 1%. Our model thus enables the rapid identification of variants and mutations that affect transmission from genomic surveillance data.

Impact of The COVID-19 Pandemic on the Malaria Burden in Northern Ghana: Analysis of Routine Surveillance Data

Introduction: The COVID-19 pandemic and its collateral damage severely impact health systems globally and risk to worsen the malaria situation in endemic countries. Malaria is a leading cause of morbidity and mortality in Ghana. This study aims to analyze routine surveillance data to assess possible effects on the malaria burden in the first year of the COVID-19 pandemic in the Northern Region of Ghana. Methods: Monthly routine data from the District Health Information Management System II (DHIMS2) of the Northern Region of Ghana were analyzed. Overall outpatient department visits and malaria incidence rates from the years 2015 to 2019 were compared to the corresponding data of the year 2020. Results: Compared to the corresponding periods of the years 2015 to 2019, overall visits and malaria incidence in pediatric and adult outpatient departments in northern Ghana decreased in March and April 2020, when major movement and social restrictions were implemented in response to the pandemic. Incidence slightly rebounded afterwards in 2020 but stayed below the average of the previous years. Data from inpatient departments showed a similar but more pronounced trend when compared to outpatient departments. In pregnant women, however, malaria incidence in outpatient departments increased after the first COVID-19 wave. Discussion: The findings from this study show that the COVID-19 pandemic affects the malaria burden in health facilities of Ghana, with declines in in- and outpatient rates. Pregnant women may experience reduced access to intermittent preventive malaria treatment and insecticide treated nets, resulting in subsequent higher malaria morbidity. Further data from other African countries, particularly on community-based studies, are needed to fully determine the impact of the pandemic on the malaria situation.

Descriptive Analysis of Typhoid Fever Surveillance Data in the Jimma Zone, Southwest Ethiopia (2015–2019)

Introduction. Typhoid fever is a major cause of morbidity and mortality around the globe, and it is a serious illness in developing countries. Typhoid fever is prevalent in Ethiopia, and the burden differs with diverse demography, environment, and climate. The study aimed to determine the incidence of typhoid fever cases by person, place, and time. Method. A descriptive cross-sectional study was conducted among the five years (2015–2019) of surveillance data of typhoid fever in the Jimma Zone, Oromia Region, Ethiopia. The data were extracted from the zonal health management information system database from May to June 2020. SPSS version 21 was used to enter and analyze the data. Descriptive analysis was used to assess the distribution of typhoid fever incidence in time, place, and personal groups. Result. A total of 36,641 individuals suffered from typhoid fever during the five years. Among these, 18,972 (51.8%) were females and 17,669 (48.2%) were males. Incidence of typhoid fever was found as follows: 216, 198, 203, 264, and 299 cases per 100,000 persons were reported during 2015, 2016, 2017, 2018, and 2019, respectively. Typhoid fever cases were increased by 1.4 from 2015–2019. A high incidence of cases was observed at the start of wet months. The majority of the investigated cases were identified in Kersa, 4,476 (12.2%), Gomma, 4,075 (11.1%), and Mana, 3,267 (8.9%), woredas. Of the total, 151 (0.4%) of the reported cases were admitted for inpatient care. During the five years of surveillance data, death was not reported from all woredas. Conclusion and Recommendation. Typhoid fever was a major public health problem in the Jimma Zone for the last 5 years, and it was increased through the years. Zonal health departments should strengthen the interventions focused on the woredas that had a high burden of typhoid fever at the start of the wet months.