Sheetal Prakash Silal

Malaria programmes increasingly rely on modelled evidence to support intervention prioritisation, resource allocation, and elimination planning, yet a persistent gap remains between technical modelling outputs and their use in decision-making. We describe the design, implementation, and early outcomes of the Regional Malaria Modelling Translational Fellowship, a six-month executive education programme delivered in 2025 to 21 fellows nominated by national malaria programmes in seven African countries. The Fellowship was designed to strengthen translational capacity by focusing on question formulation, systems thinking, model design and critique, interpretation of outputs, uncertainty, health economics, communication, and stakeholder engagement. The hybrid structure combined three intensive in-person blocks with regular virtual sessions. Country-teams work on capstone projects throughout the Fellowship, applying learnings to develop policy-relevant modelling proposals aligned with national malaria priorities. The programme was accredited as a University of Cape Town short course, which supported credibility, participant commitment, and institutional endorsement. Early evaluation showed improvements across all competency domains, with the largest gains in fellows’ confidence in applying modelling to decision-making, translating model findings into recommendations, and communicating technical results to non-technical audiences. Qualitative feedback suggested that the Fellowship helped shift participants’ engagement with modelling from passive acceptance of results toward critical interpretation, collaborative dialogue, and practical application. These findings suggest that translational, executive-style training can strengthen the interface between modelling and malaria policy. The publicly available curriculum offers a replicable framework that may be adapted for other infectious disease and public health settings where modelling evidence is increasingly central to decision-making. Highlights: Strengthened national malaria programmes’ capacity to use modelled evidence. An accredited six-month fellowship trained 21 decision-makers from seven countries. Country-led capstone projects applied learning to national malaria priorities. Fellows gained confidence in interpreting and communicating model outputs. The open curriculum offers a replicable model for other disease programmes.

Artemisinin partial resistance has not yet been reported in southern Africa. Therefore, the magnitude of the spread of artemisinin partial resistance in this region is yet to be quantified. Using a two strain metapopulation modelling framework, we explored possible spread of artemisinin partial resistance in eight connected countries with high level of human movement. We explored three scenarios in which artemisinin partial resistance may first enter circulation: low malaria transmission level country; high malaria transmission level country and all countries and compared to an artemisinin partial resistance free scenario. Partial rank correlation coefficient sensitivity analysis was performed to identify key parameters that drive artemisinin partial resistance spread. Our model simulations show that high mobility between countries can increase the spread of mutations associated with delayed clearance. Suggesting that artemisinin partial resistance will be confirmed (>5% partial resistant cases) after 14 years of circulation if it is to appear in southern Africa. We confirm that human movement, both human-to-mosquito and mosquito-to-human probabilities of transmission, were significant and highly sensitive parameters in the spread of artemisinin partial resistance. Human mobility between countries can facilitate the spread of artemisinin partial resistance. More research is needed to identify strategies to preserve the efficacy of artemisinin-based combination therapies in the presence of partial artemisinin resistance, which may eventually lead to treatment failure and necessitate regimen replacement. Author summary: We developed a two-strain, spatially explicit metapopulation model with eight patches of low, moderate and high levels of malaria transmission. We simulated the spread of artemisinin partial resistance in geographically connected Southern African Development Community Malaria Elimination 8 Initiative (SADC-E8) countries with a high-level of human cross border movement. This study provides a hypothetical reference for future studies necessary to fully understand the spread and impact of artemisinin partial resistance in the malaria elimination process in southern Africa. We found that human movement between countries facilitates the spread of artemisinin partial resistance. The slow parasite clearance and high gametocyte carriage (2-fold of sensitive strain 3 days after ACTs treatment) drive the spread of artemisinin partial resistance within the human population. In the absence of artemisinin partial resistance data in SADC-E8, this study’s simulations inform policy to make decisions on the potential spread of artemisinin partial resistance.

Tetanus is a severe disease of the nervous system, transmitted through bacteria in the environment. In the absence of medical attention, case fatality rates are extremely high. Despite progress towards maternal and neonatal tetanus elimination targets, tetanus remains a serious public health problem. Routine infant and maternal vaccination have contributed to considerable reduction in cases and deaths from tetanus globally. However, protective immunity wanes over time. To increase duration of protection, the World Health Organization recommends three diphtheria-tetanus-pertussis-containing vaccine booster doses be given in early childhood, childhood, and adolescence. Evidence to support country-level decision-making about the introduction of these booster doses is critical. We have developed a novel age-structured, deterministic compartmental model of tetanus transmission and vaccination. The model is driven by environmental transmission and incorporates interventions like hygiene and safe birth practices to reduce the magnitude of environmental transmission. It explicitly models vaccination, separating each dose of the primary series, booster doses, and maternal vaccination to capture dose-specific effectiveness and duration of protection. The model captures heterogeneous immunity profiles by dose and age, and the cumulative nature of vaccine-derived protection. The immune dynamics follow the patterns described in literature and can replicate seroprevalence studies, although the exact characterisation of immunity in the literature still has gaps. This model presents a substantial advancement on previously published models and is well positioned to inform tailored vaccination strategies to reduce neonatal and non-neonatal tetanus.

Pertussis remains an endemic and periodically resurgent vaccine-preventable disease despite long-standing immunisation programmes, reflecting complex interactions between transmission, waning immunity, vaccination history, and heterogeneous clinical presentation. We present a comprehensive age-structured mathematical model of pertussis transmission that explicitly represents infection heterogeneity, immunity dynamics, and detailed vaccination schedules across the life course. The model stratifies the population into 56 age groups and 29 epidemiological states, capturing four distinct infection types that differ by severity, symptoms, and infectiousness, including asymptomatic infection. Both naturally acquired and vaccine-derived immunity are modelled as non-lifelong, incorporating waning, partial protection, reinfection, and immune boosting following exposure without transmissible infection. Vaccination is represented at high resolution, including dose-specific primary series vaccination, booster doses in early childhood, childhood, and adolescence, and maternal immunisation during pregnancy, with differentiation between whole-cell and acellular pertussis vaccine formulations and historical changes in vaccine use and coverage. Periodicity and stochasticity are incorporated to reproduce observed multi-year epidemic cycles. A global sensitivity analysis using Latin hypercube sampling and partial rank correlation coefficients identifies immunity waning rates, immune boosting, and recovery from severe infection as key drivers of modelled incidence, mortality, and population protection. By integrating detailed immune processes with realistic vaccination histories, this model provides a flexible framework for evaluating pertussis epidemiology and assessing the population-level impact of alternative vaccination strategies, including booster and maternal immunisation policies.

Background: In Cameroon, malaria is a significant public health issue, with heterogenous distribution and seasonal change making control and containment harder to plan. Medium-term projections are needed to provide reliable early warning and optimal prevention resource allocation. Traditional methods, including ARIMA and SARIMA, are dogged by noisy surveillance data as well as structural non-stationarity, and the hybrids currently being applied rarely measure the uncertainty in the forecasts. Methods: We introduce a multi-step hybrid forecasting pipeline to combine wavelet-based denoising, robust Seasonal-Trend decomposition with Loess (STL), and state-of-the-art remainder modeling. The remaining component was decomposed by using ARIMA, SARIMA, or Bayesian Structural Time Series (BSTS) and forecasts were recreated out of all components. The analysis was conducted on monthly malaria incidence in the ten administrative regions of Cameroon and 24-month future projections were developed. RMSE, MAE, R2, and information criteria were used to evaluate model performance, and uncertainty was measured using analytical intervals (ARIMA/SARIMA) and posterior predictive distributions (BSTS). Results: The Wavelet STL preprocessing significantly enhanced model stability, and model accuracy in all regions. ARIMA and SARIMA models performed similarly, and R2 values were typically between 0.49 and 0.77, indicating the usefulness of STL in eliminating seasonal effects. In many areas BSTS was significantly higher or as large as ARIMA/SARIMA, and obtained higher R2 values. Notably, BSTS offered probabilistic predictions that were calibrated, which allowed to effectively measure the forecast uncertainty. The results presented in this paper indicate that the hybrid pipeline suggested is both noisy and uncertain, and can provide forecasts of malaria cases in the villages of Cameroon over a period of 24 months with high confidence. Conclusion: The Wavelet-STL hybrid model is the next step in malaria prediction by combining the denoising, structural decomposition, and probabilistic models. Its deployment in the regions of Cameroon shows innovative methodological value and direct applicability to early warning systems. The method can be easily extended to other infectious diseases with seasonal spread and noisy surveillance data.

Although Zambia does not rank among the highest contributors to global malaria cases, in 2023, it had approximately eleven million cases. Furthermore, Zambia’s malaria incidence distribution is spatially heterogeneous at fine scales such as health facility catchment areas (HFCAs). HFCA-level malaria incidence stratification currently guides implementation and targeting approaches. We aim to enhance the current stratification approach by incorporating archetypical characteristics that drive malaria, including environmental, access, epidemiological, and socioeconomic factors. An archetype-based stratification approach may inform the tailoring of interventions to accelerate progress toward elimination. We used literature-informed data from 2017 to 2024 from the Zambia Health Information Management System and publicly available datasets. A zero-inflated negative binomial mixed-effects regression model was fitted to identify significant variables influencing malaria heterogeneity at the HFCA level. The significant variables were used to archetype HFCAs using the Clustering Large Applications (CLARA) algorithm. Significant variables included: walking travel time to the nearest health facility; percentage of malaria attributed to pregnant women; percentage of malaria attributed to children under five (U5); Children U5’s bednet (Long-lasting insecticide-treated nets or Insecticide-treated nets) coverage; bednet use rate; elevation; enhanced vegetation index; precipitation; and housing quality. Four new archetypes were derived. Relative to malaria incidence-based stratification, the archetype-based stratification exhibited spatiotemporal stability over time. Across all four new archetypes, elevation, bednet use rate, and housing quality emerged as the most influential features. However, all four archetypes exhibited distinct features influencing their makeup. The study proposes embedding archetypical characteristics in the malaria incidence-based stratification approach to better inform intervention tailoring and prioritisation based on the distinct archetype features driving malaria at the HFCAs level. Furthermore, the study provides a precursor for simulating potential intervention mixes based on malaria transmission archetypical characteristics, which can provide decision-makers with projected impact of varied investments in HFCAs with similar archetypical features.

Background: Malaria remains a major public health issue in sub-Saharan Africa, particularly in Benin, where it is the leading cause of morbidity and mortality. Despite ongoing control efforts, the disease burden remains high, necessitating a more comprehensive approach to explore differents pathways to malaria control in the northern regions (Atacora, Alibori, Borgou, Donga) of Benin Republic. This study employs a metapopulation mathematical model to assess the impact of key interventions, including insecticide-treated nets (ITNs), seasonal malaria chemoprevention (SMC), intermittent preventive treatment in pregnancy (IPTp), and indoor residual spraying (IRS) and effective anti-malaria treatment in the northern regions of Benin. Methods: A metapopulation model stratified by age groups was developed to simulate malaria transmission dynamics in four highly endemic regions: Atacora, Alibori, Borgou, and Donga. The model incorporates seasonal variations, vector control measures, and treatment interventions. Calibration was performed using malaria incidence and prevalence data of Malaria Atlas Project from 2010 to 2022. Various intervention scenarios were simulated to determine their potential impact on malaria control. Results: Among the intervention scenarios tested, the combination of all five strategies ITNs, SMC, IPTp, IRS, and treatment of malaria cases was predicted to be the most effective, reducing malaria incidence to a low level. In addition, scaling up ITN coverage in combination with effective anti-malaria treatment emerged as the second most effective strategy, reducing malaria cases, though it was less impactful than the comprehensive approach involving all five interventions. Other strategies, such as ITNs alone or combined with SMC also showed significant reductions in malaria burden particularly among children.

In Zambia-like settings, asymptomatic and clinical carriers not seeking treatment further complicate malaria elimination, making reactive case detection (RCD) essential for identifying undetected infections. However, RCD faces operational hurdles, including resource shortages, logistical challenges, limited community health workers (CHWs), and limitations in availability and sensitive rapid diagnostic tests (RDTs). Prioritizing specific improvement measures is critical to enhance intervention outcomes. A mathematical model of malaria transmission for low-transmission areas (fewer than 200 cases per 1,000 annually) was developed using published data to simulate RCD. This model assessed the impact of potential improvement measures designed to address the identified operational challenges affecting RCD. Improvement measures included increasing CHWs, adjusting response times, improving RDT sensitivity, and incorporating focal mass drug administration (fMDA). A shortage of CHWs and limited availability of RDTs have the most negative impact on RCD’s ability to reduce cases. In scenarios where CHWs or RDT availability for RCD were reduced by 50%, annual cases increased by approximately 22%. Only the incorporation of fMDA as an improvement measure succeeded countering the situation, resulting in a 43% reduction. Increasing CHWs to offset RCD inefficiencies caused by limited RDT sensitivity and difficulties finding individuals reduced cases by approximately 13 and 14%, respectively, reducing more cases than improving reaction time or increasing the screening radius. Although RCD is prone to challenges, the manipulation of improvement measures such as CHWs and fMDA provides promise for RCD to contribute towards malaria elimination. However, the participation of CHWs is voluntary and primarily motivated by informal incentives, often provided by donors. Finding sustainable means to ensure the sufficient availability of CHWs may guarantee continued RCD contributions toward maintaining stable malaria prevalence. More research is required to explore the application of RCD in archetypical transmission areas suitable for RCD as improvement measures to the identified challenges hindering RCD.