Michael Emch

HIV transmission during early HIV infection impedes efforts to end HIV as a public health threat, as diagnosis typically occurs after this period of elevated transmission risk. To guide diagnosis and prevention strategies, we evaluated the geospatial and phylogenetic clustering of acute and recent HIV infection in Lilongwe, Malawi. We identified people with acute (pre-seroconversion) HIV infection (AHI) and a random sample of people with post-acute HIV infection who presented to a sexually transmitted infections (STI) clinic in Lilongwe, Malawi between 2015 and 2019. We evaluated infection recency in people with post-acute HIV using a LAg-Avidity assay. We mapped the household locations of people with AHI and identified geospatial clusters using a flexible scan statistic. We constructed consensus sequences from deep sequencing reads to identify phylogenetic clusters through genetic distance thresholds and maximum likelihood trees. We identified 141 people with AHI, 30 people with recent HIV, and 652 people with chronic (non-recent) HIV. We identified four geospatial clusters that contained the residences of 30% of clinic attendees with AHI, despite comprising just 0.8% of the populated land area and 3.5% of the population. We also identified fourteen distinct two-person phylogenetic clusters. Ten of the fourteen were male-female pairs, nine of which were clinic referral pairs. The remaining four were same-sex pairs who had not referred each other to the clinic and may have been missing network intermediaries. Three of the fourteen phylogenetic pairs consisted of only acute/recent members, and zero phylogenetic linkages were located within geospatial clusters. AHI detection programs anchored in STI clinic populations and their neighborhoods could facilitate identification of early HIV infection, enabling treatment initiation and transmission prevention efforts during this most infectious period. Future studies of intervention packages and deployment approaches can help inform the optimal design and implementation of AHI-focused strategies for reducing HIV incidence.

Background Zanzibar has achieved historic reductions in malaria incidence, but high connectivity to mainland Tanzania and imported cases continue to pose a challenge to “last mile” malaria elimination. Methods To understand factors driving malaria importation, we collected travel histories and demographics of malaria cases presenting to 94 health facilities across Zanzibar’s main island, Unguja, from 2022–2023. We also used two other sources of mobility data—self-reported travel at the outbound Dar es Salaam ferry terminal and Meta Data for Good colocation and movement distribution—to examine movement patterns between Unguja and mainland Tanzania. We integrated these with CHIRPS rainfall data to explore the seasonality of human movement and travel-associated malaria risk. Results Among 1,155 malaria cases with recent travel to mainland Tanzania, travel to Tanga (21%), Dar es Salaam (20%), and Morogoro (15%) were most common. Nearly half of travelers had spent over 14 nights away from home; the majority were visiting family. While mainlanders made up two-thirds of ferry travelers, 28% of Unguja malaria cases reported primary residence on the mainland; these were young adult males working in farming as well as children under age 5. Long-distance travel off the island decreased during the rainy seasons, while rainfall in mainland destinations correlated with imported malaria cases. With different biases, colocation and ferry data reflected the proportion of cases imported from different malaria risk regions on the mainland. Discussion Movement flows and seasonal rainfall patterns drive imported malaria in predictable ways that can be harnessed to target high-risk travelers for intervention.

Influenza A viruses (IAVs) pose a major public health threat due to their wide host range and pandemic potential. Pigs have been proposed as "mixing vessels" for avian, swine, and human IAVs, significantly contributing to influenza ecology. In the United States, IAVs are enzootic in commercial swine farming operations, with numerous genetic and antigenic IAV variants having emerged in the past two decades. However, the dynamics of intensive swine farming systems and their interactions with ecological factors influencing IAV evolution have not been systematically analysed. This review examines the evolution of swine IAVs in commercial farms, highlighting the role of multilevel ecological factors. A total of 61 articles published after 2000 were reviewed, with most studies conducted after 2009 in Midwestern US, followed by Southeast and South-central US. The findings reveal that ecological factors at multiple spatial scales, such as regional transportation networks, interconnectedness of swine operations, farm environments, and presence of high-density, low-genetic diversity herds, can facilitate virus transmission and enhance virus evolution. Additionally, interactions at various interfaces, such as between commercial swine and feral swine, humans, or wild birds contribute to the increase in genetic diversity of swine IAVs. The review underscores the need for comprehensive studies and improved data collection to better understand the ecological dynamics influencing swine IAV evolution. This understanding is crucial for mitigating disease burden in swine production and reducing the risk of zoonotic influenza outbreaks.

Understanding influential factors for fecal contamination in groundwater is critical for ensuring water safety and public health. The objective of this study is to identify key factors for fecal contamination of shallow tubewells using machine learning methods. Three methods, including recursive feature elimination (RFE) with XGBoost, Random Forest, and mutual information, were implemented to examine E. coli presence and concentration in 1495 tubewell water samples in Matlab, Bangladesh. For E. coli presence, climatic variables, including average rainfall and temperature over the 30, 15, and 7 days preceding sampling, as well as ambient temperature and rainfall on the sampling day, emerged as critical predictors. Land cover characteristics, such as the percentages of urban and agricultural areas within 100 m of a tubewell, were also significant. For E. coli concentration, land cover characteristics within 100 m, the number of hot and heavy-rain days in the 30 days preceding sampling, average rainfall and temperature in the 3 days preceding sampling, and ambient temperature on the sampling day were identified as key drivers. Random Forest and mutual information yielded results that were more similar to each other than to those of RFE with XGBoost. The findings highlight the interplay between climatic factors, land use, and population density in determining fecal contamination in shallow well water and demonstrate the power of machine learning algorithms in ranking these factors.

Purpose The Democratic Republic of Congo has one of the highest burdens of malaria in the world, accounting for 12.3% of malaria cases and 11.6% of malaria deaths. The country has been scaling up various malaria control interventions, but how the malaria burden and risk factors have evolved remains unclear. This study aimed to estimate the trend in the burden of malaria in different settings in Kinshasa Province and assess factors contributing to the infection. Participants From 2018 to 2022, a cohort study was conducted in three health areas in Kinshasa Province known to have varying malaria endemicities: Voix du Peuple (urban), Kimpoko (peri-urban), and Bu (rural) health areas in Kinshasa Province. Participants 6 months of age and older were recruited and followed biannually through household visits and encouraged to visit study health centres whenever they felt ill. The participants’ sociodemographic data, illness history and access to malaria interventions were collected along with blood specimens. During the same study period, mosquitoes and larvae were collected to study entomological characteristics that may contribute to malaria transmission. Findings A total of 1635 participants were recruited from 239 households. The median number of participants per household was 6 (IQR, 5–9). Over half (54.7%) of the participants were female, 14.6% were under-5 children, and 33.3% were 5–14 years old. Household net ownership and reported use the previous night were 77.8% and 79.1%, respectively. Overall, malaria prevalence by rapid diagnostic test at baseline was 33.4%. Of the recruited participants, 1390 (85%) completed the 4 years of follow-up. Future plans Planned analyses include calculating malaria prevalence and incidence trends at follow-up as well as risk factors for infection. Continued analyses will link epidemiological and entomological data to understand malaria transmission evolution and the sustained high prevalence of malaria over time in various settings.

Reports suggest non-falciparum species are an underappreciated cause of malaria in sub-Saharan Africa but their epidemiology is ill-defined, particularly in highly malaria-endemic regions. We estimated incidence and prevalence of PCR-confirmed non-falciparum and Plasmodium falciparum malaria infections within a longitudinal study conducted in Kinshasa, Democratic Republic of Congo (DRC) between 2015-2017. Children and adults were sampled at biannual household surveys and routine clinic visits. Among 9,089 samples from 1,565 participants, incidences of P. malariae, P. ovale spp., and P. falciparum infections by 1-year were 7.8% (95% CI: 6.4%-9.1%), 4.8% (95% CI: 3.7%-5.9%) and 57.5% (95% CI: 54.4%-60.5%), respectively. Non-falciparum prevalences were higher in school-age children, rural and peri-urban sites, and P. falciparum co-infections. P. falciparum remains the primary driver of malaria in the DRC, though non-falciparum species also pose an infection risk. As P. falciparum interventions gain traction in high-burden settings, continued surveillance and improved understanding of non-falciparum infections are warranted.

An investigation was carried out to examine the use of national Xpert MTB/RIF data (2013–2017) and GIS technology for MTB/RIF surveillance in South Africa. The aim was to exhibit the potential of using molecular diagnostics for TB surveillance across the country. The variables analysed include Mycobacterium tuberculosis (Mtb) positivity, the mycobacterial proportion of rifampicin-resistant Mtb (RIF), and probe frequency. The summary statistics of these variables were generated and aggregated at the facility and municipal level. The spatial distribution patterns of the indicators across municipalities were determined using the Moran’s I and Getis Ord (Gi) statistics. A case-control study was conducted to investigate factors associated with a high mycobacterial load. Logistic regression was used to analyse this study’s results. There was striking spatial heterogeneity in the distribution of Mtb and RIF across South Africa. The median patient age, urban setting classification, and number of health care workers were found to be associated with the mycobacterial load. This study illustrates the potential of using data generated from molecular diagnostics in combination with GIS technology for Mtb surveillance in South Africa. Spatially targeted interventions can be implemented in areas where high-burden Mtb persists.