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Abstract Many studies have projected malaria risks with climate change scenarios by modelling one or two environmental variables and without the consideration of malaria control interventions. We aimed to predict the risk of malaria with climate change considering the influence of rainfall, humidity, temperatures, vegetation, and vector control interventions (indoor residual spraying (IRS) and long-lasting insecticidal nets (LLIN)). We used negative binomial models based on weekly malaria data from six facility-based surveillance sites in Uganda from 2010–2018, to estimate associations between malaria, environmental variables and interventions, accounting for the non-linearity of environmental variables. Associations were applied to future climate scenarios to predict malaria distribution using an ensemble of Regional Climate Models under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Predictions including interaction effects between environmental variables and interventions were also explored. The results showed upward trends in the annual malaria cases by 25% to 30% by 2050s in the absence of intervention but there was great variability in the predictions (historical vs RCP 4.5 medians [Min–Max]: 16,785 [9,902–74,382] vs 21,289 [11,796–70,606]). The combination of IRS and LLIN, IRS alone, and LLIN alone would contribute to reducing the malaria burden by 76%, 63% and 35% respectively. Similar conclusions were drawn from the predictions of the models with and without interactions between environmental factors and interventions, suggesting that the interactions have no added value for the predictions. The results highlight the need for maintaining vector control interventions for malaria prevention and control in the context of climate change given the potential public health and economic implications of increasing malaria in Uganda.

Introduction Maps of malaria risk are important tools for allocating resources and tracking progress. Most maps rely on cross-sectional surveys of parasite prevalence, but health facilities represent an underused and powerful data source. We aimed to model and map malaria incidence using health facility data in Uganda.Methods Using 24 months (2019–2020) of individual-level outpatient data collected from 74 surveillance health facilities located in 41 districts across Uganda (n=445 648 laboratory-confirmed cases), we estimated monthly malaria incidence for parishes within facility catchment areas (n=310) by estimating care-seeking population denominators. We fit spatio-temporal models to the incidence estimates to predict incidence rates for the rest of Uganda, informed by environmental, sociodemographic and intervention variables. We mapped estimated malaria incidence and its uncertainty at the parish level and compared estimates to other metrics of malaria. To quantify the impact that indoor residual spraying (IRS) may have had, we modelled counterfactual scenarios of malaria incidence in the absence of IRS.Results Over 4567 parish-months, malaria incidence averaged 705 cases per 1000 person-years. Maps indicated high burden in the north and northeast of Uganda, with lower incidence in the districts receiving IRS. District-level estimates of cases correlated with cases reported by the Ministry of Health (Spearman’s r=0.68, p

Abstract Background In Uganda, artemether-lumefantrine (AL) is first-line therapy and dihydroartemisinin-piperaquine (DP) second-line therapy for the treatment of uncomplicated malaria. This study evaluated the efficacy and safety of AL and DP in the management of uncomplicated falciparum malaria and measured the prevalence of molecular markers of resistance in three sentinel sites in Uganda from 2018 to 2019. Methods This was a randomized, open-label, phase IV clinical trial. Children aged 6 months to 10 years with uncomplicated falciparum malaria were randomly assigned to treatment with AL or DP and followed for 28 and 42 days, respectively. Genotyping was used to distinguish recrudescence from new infection, and a Bayesian algorithm was used to assign each treatment failure a posterior probability of recrudescence. For monitoring resistance, Pfk13 and Pfmdr1 genes were Sanger sequenced and plasmepsin-2 copy number was assessed by qPCR. Results There were no early treatment failures. The uncorrected 28-day cumulative efficacy of AL ranged from 41.2 to 71.2% and the PCR-corrected cumulative 28-day efficacy of AL ranged from 87.2 to 94.4%. The uncorrected 28-day cumulative efficacy of DP ranged from 95.8 to 97.9% and the PCR-corrected cumulative 28-day efficacy of DP ranged from 98.9 to 100%. The uncorrected 42-day efficacy of DP ranged from 73.5 to 87.4% and the PCR-corrected 42-day efficacy of DP ranged from 92.1 to 97.5%. There were no reported serious adverse events associated with any of the regimens. No resistance-associated mutations in the Pfk13 gene were found in the successfully sequenced samples. In the AL arm, the NFD haplotype (N86Y, Y184F, D1246Y) was the predominant Pfmdr1 haplotype, present in 78 of 127 (61%) and 76 of 110 (69%) of the day 0 and day of failure samples, respectively. All the day 0 samples in the DP arm had one copy of the plasmepsin-2 gene. Conclusions DP remains highly effective and safe for the treatment of uncomplicated malaria in Uganda. Recurrent infections with AL were common. In Busia and Arua, the 95% confidence interval for PCR-corrected AL efficacy fell below 90%. Further efficacy monitoring for AL, including pharmacokinetic studies, is recommended. Trial registration The trail was also registered with the ISRCTN registry with study Trial No. PACTR201811640750761

Abstract Background Accurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviours are not well defined. This study’s aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility. Methods Outpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a 3-year period (2011–2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts. Results A total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p

Abstract Background Understanding the relationship between malaria infection risk and disease outcomes represents a fundamental component of morbidity and mortality burden estimations. Contemporary data on severe malaria risks among populations of different parasite exposures are scarce. Using surveillance data, we compared rates of paediatric malaria hospitalisation in areas of varying parasite exposure levels. Methods Surveillance data at five public hospitals; Jinja, Mubende, Kabale, Tororo, and Apac were assembled among admissions aged 1 month to 14 years between 2017 and 2018. The address of each admission was used to define a local catchment population where national census data was used to define person-year-exposure to risk. Within each catchment, historical infection prevalence was assembled from previously published data and current infection prevalence defined using 33 population-based school surveys among 3400 children. Poisson regression was used to compute the overall and site-specific incidences with 95% confidence intervals. Results Both current and historical Plasmodium falciparum prevalence varied across the five sites. Current prevalence ranged from

Global health security depends on effective surveillance for infectious diseases. In Uganda, resources are inadequate to support collection and reporting of data necessary for an effective and responsive surveillance system. We used a cross-cutting approach to improve surveillance and laboratory capacity in Uganda by leveraging an existing pediatric inpatient malaria sentinel surveillance system to collect data on expanded causes of illness, facilitate development of real-time surveillance, and provide data on antimicrobial resistance. Capacity for blood culture collection was established, along with options for serologic testing for select zoonotic conditions, including arboviral infection, brucellosis, and leptospirosis. Detailed demographic, clinical, and laboratory data for all admissions were captured through a web-based system accessible at participating hospitals, laboratories, and the Uganda Public Health Emergency Operations Center. Between July 2016 and December 2017, the expanded system was activated in pediatric wards of 6 regional government hospitals. During that time, patient data were collected from 30,500 pediatric admissions, half of whom were febrile but lacked evidence of malaria. More than 5,000 blood cultures were performed; 4% yielded bacterial pathogens, and another 4% yielded likely contaminants. Several WHO antimicrobial resistance priority pathogens were identified, some with multidrug-resistant phenotypes, including Acinetobacter spp., Citrobacter spp., Escherichia coli, Staphylococcus aureus, and typhoidal and nontyphoidal Salmonella spp. Leptospirosis and arboviral infections (alphaviruses and flaviviruses) were documented. The lessons learned and early results from the development of this multisectoral surveillance system provide the knowledge, infrastructure, and workforce capacity to serve as a foundation to enhance the capacity to detect, report, and rapidly respond to wide-ranging public health concerns in Uganda.