1 — Introduction
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1.1 Background and rationale
Typhoid fever, caused by Salmonella enterica serovar Typhi, constitutes a substantial and insufficiently addressed global public-health challenge. The World Health Organization estimates 11–20 million illness episodes and 128,000–161,000 deaths annually, with the burden concentrated in low- and middle-income countries (LMICs) across South and Southeast Asia and sub-Saharan Africa (World Health Organization, 2018). Transmitted primarily through ingestion of food or water contaminated with fecal matter, typhoid is fundamentally a disease of inadequate water, sanitation, and hygiene (WASH) infrastructure, and is therefore acutely sensitive to climate variability and change (Bao et al., 2023; Crump et al., 2004).
Nepal, a mountainous, landlocked nation of approximately 30 million people in the Hindu Kush Himalayan region, bears a disproportionately high burden of typhoid fever relative to its income level. HMIS data maintained by the Ministry of Health and Population consistently rank enteric fever among the top five reported communicable diseases in outpatient departments, with hundreds of thousands of cases documented each year across all seven provinces and 77 administrative districts (Ministry of Health and Population, Government of Nepal, 2023). The true incidence likely substantially exceeds HMIS-reported figures owing to recognized limitations in healthcare-seeking behavior, diagnostic capacity, and clinical overlap with malaria, dengue, and undifferentiated viral fever (Bhatt et al., 2023; Parry et al., 2002).
Nepal’s typhoid epidemiology is inextricably linked to the country’s climate system. The southwest monsoon delivers approximately 75–80 % of annual precipitation within a four-month window (June–September), overwhelming aging water-supply and sanitation infrastructure and creating conditions conducive to fecal contamination of drinking-water sources (Bhatt et al., 2023; Ministry of Health and Population, Government of Nepal, 2023). Temperature, humidity, and monsoon flooding amplify transmission through pathogen biology, fly-vector dynamics, food spoilage, and water-source contamination (Bao et al., 2023; Crump et al., 2004; Hashizume et al., 2010; NPDRR Nepal, 2025).
Climate change represents an intensifying structural threat. Nepal’s National Adaptation Plan 2021–2050 (Government of Nepal, 2021) projects mean temperatures will increase 1.5–3.5 °C above pre-industrial levels by 2100, with monsoon rainfall intensifying and extreme precipitation events becoming more frequent (Intergovernmental Panel on Climate Change, 2022; Ministry of Forests and Environment, Government of Nepal, 2021). Machine-learning methods, with their capacity to model complex non-linear relationships without restrictive parametric assumptions, offer a promising framework for climate-informed disease prediction (Breiman, 2001; Chen & Guestrin, 2016).
1.2 Problem statement
Despite the well-established environmental sensitivity of typhoid fever and the documented impact of monsoon climate variability on its seasonal dynamics in Nepal, four interrelated gaps persist:
- HMIS surveillance and systematic climate datasets have rarely been integrated within formal predictive modeling studies; existing analyses are limited in geographic scope or apply only cross-sectional regression that does not capture lag structure.
- The delayed nature of the climate–typhoid association is inadequately captured by contemporaneous analyses.
- The relative contributions of climatic drivers (precipitation, temperature, humidity), flood events, and autoregressive case dynamics have not been rigorously disentangled in the Nepali context.
- No validated district-level predictive model exists to support climate-informed early-warning systems.
1.3 Research objectives
- Analyze temporal trends and seasonal patterns of typhoid incidence across Nepal’s 77 districts (2015–2023).
- Examine the relationship between typhoid incidence and climate variables under both contemporaneous and lagged specifications.
- Develop a feature-engineered dataset with lagged climate variables, rolling averages, seasonal indicators, and temporal encodings.
- Develop and compare three machine-learning models (Random Forest, XGBoost, Multilayer Perceptron) and evaluate them using RMSE, MAE, R², and MAPE.
- Identify the most influential predictors and discuss policy implications for climate-adaptive public health planning.
1.4 Research questions
- What are the dominant temporal and seasonal patterns in district-level typhoid incidence in Nepal between 2015 and 2023?
- What is the magnitude, direction, and temporal structure of the association between climate variables and district-level typhoid incidence?
- Which of Random Forest, XGBoost, or MLP provides the most accurate and generalizable predictions on temporally held-out test data?
- Which features exert the greatest predictive influence according to normalized XGBoost feature-importance analysis?
- What are the public-health and climate-adaptation policy implications of a validated district-level prediction model?
1.5 Significance
Scientifically, this is the first systematic, national-scale, district-level multi-model ML study of typhoid fever in Nepal, integrating four validated institutional datasets within a unified analytical framework. Methodologically, the study demonstrates that three structurally distinct ML architectures converge within 0.01 R² when trained on carefully engineered lag features — establishing that the climate × autoregressive signal sets the predictive ceiling. From a policy perspective, the study creates the evidentiary foundation for climate-informed typhoid early-warning systems, targeted pre-monsoon WASH interventions, and evidence-based typhoid conjugate vaccine (TCV) campaign prioritization aligned with the NAP (Government of Nepal, 2021).
1.6 Scope
This study covers all 77 administrative districts of Nepal for January 2015 – December 2023, analyzing monthly district-level data (N = 7,327 valid district-month observations). The unit of analysis is the district-month, and the primary outcome is the count of outpatient enteric-fever cases reported through HMIS. The analytical scope encompasses four predictor domains: climate (precipitation, temperature, humidity), extreme hydrometeorological events (flood frequency), seasonal structure (monsoon indicator, cyclical month encoding), and epidemiological autoregression (prior month’s case count). The study does not incorporate individual-level data, vaccination coverage, or district-level WASH coverage; the analysis is ecological in design.
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