1 — Introduction
Thesis · APA section
Thesis · Section 1 of 7 · Paper landing
1.1 Background and rationale
Typhoid fever, an acute systemic febrile illness 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 worldwide, with the overwhelming majority of this 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 from infected individuals, typhoid is fundamentally a disease of inadequate water, sanitation, and hygiene (WASH) infrastructure, and is therefore acutely sensitive to the environmental conditions that govern water quality and sanitation system performance — both of which are profoundly influenced by 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. Annual 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 is widely believed to substantially exceed HMIS-reported figures owing to recognized limitations in healthcare-seeking behavior, diagnostic capacity at peripheral facilities, and the clinical overlap of typhoid 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 Nepal’s annual precipitation within a four-month window (June–September), overwhelming aging water-supply and sanitation infrastructure across both urban and rural settings. The relationship between climate variability and typhoid transmission extends beyond the direct rainfall effect: ambient temperature regulates pathogen survival and replication and fly-vector dynamics (Crump et al., 2004; Hashizume et al., 2010); relative humidity modulates pathogen persistence and behavioral factors (Bao et al., 2023); extreme hydrometeorological events simultaneously contaminate multiple water sources, displace populations into degraded sanitary conditions, and disrupt routine public health services (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). A rigorous, district-level, multi-model ML study for typhoid fever in Nepal has remained absent from the literature; this thesis directly addresses that evidence gap.
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 knowledge and tool gaps persist:
- HMIS surveillance and systematic climate datasets have rarely been integrated within formal predictive modeling studies in Nepal.
- The delayed nature of the climate–typhoid association is inadequately captured by contemporaneous analyses.
- The relative contributions of climatic drivers, flood events, and autoregressive case dynamics have not been rigorously disentangled.
- No validated district-level predictive model exists to support climate-informed early-warning systems.
1.3 Research objectives
- Analyze the long-term temporal trends and seasonal patterns of typhoid incidence across Nepal’s 77 districts (HMIS 2015–2023).
- Examine the relationship between typhoid incidence and key climatic and hydrometeorological variables under both contemporaneous and lagged specifications.
- Develop a feature-engineered dataset incorporating lagged climate variables, rolling averages, seasonal indicators, and temporal encodings.
- Develop and compare four machine-learning models (Random Forest, XGBoost, MLP, Weighted Ensemble) and evaluate them using RMSE, MAE, R², and MAPE.
- Identify the most influential predictors and discuss implications for climate-informed 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 model — Random Forest, XGBoost, MLP, or Weighted Ensemble — 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, particularly with regard to implementation of the National Adaptation Plan 2021–2050 (Government of Nepal, 2021)?
1.5 Significance
Scientifically, this is the first systematic, national-scale, district-level, multi-model ML study of typhoid fever incidence in Nepal, encompassing all 77 administrative districts over nine years of HMIS surveillance. By integrating four validated institutional datasets within a unified analytical framework, the study provides an unprecedented quantitative characterization of the climate–typhoid relationship.
Methodologically, the ensemble modeling framework — combining predictions from three architecturally distinct ML models through optimization-derived weighting — demonstrates the incremental predictive gain achievable through model diversity. The reproducible, open-data pipeline can be extended to other climate-sensitive communicable diseases.
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.
1.6 Scope and limitations
The 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 and cyclical month encoding), and epidemiological autoregression (prior month’s case count).
The study does not incorporate individual-level data, vaccination coverage records, district-level WASH coverage, food-safety metrics, or antimicrobial resistance data. The analysis is ecological in design; findings describe aggregate district-level associations and should not be applied to infer individual-level risk. Limitations arising from HMIS data quality, spatial aggregation of climate variables, and the episodic nature of flood-event reporting are discussed in §3.10.
1.7 Conceptual framework
The conceptual framework describes the pathways through which climate variables and extreme hydrometeorological events influence district-level typhoid incidence, mediated through four primary mechanisms:
- Water contamination — precipitation and flooding introduce fecal matter into drinking-water sources.
- Pathogen survival and replication — temperature and humidity regulate S. Typhi environmental persistence.
- Fly-vector population dynamics — temperature and humidity govern abundance and activity of mechanical vectors.
- Food-safety margins — temperature-dependent spoilage rates increase exposure probability through contaminated food.
Each pathway operates with a temporal lag reflecting the time required for environmental contamination to translate into human exposure and for exposure to produce clinically detectable disease. The monsoon season acts as a contextual amplifier that simultaneously intensifies all four pathways. The framework further recognizes that epidemiological autoregression — the persistence of typhoid transmission from one month to the next within a district — reflects both ongoing environmental contamination and sustained chains of person-to-person fecal-oral transmission, operationalized through the lagged case-count feature (cases_lag1).
1.8 Organization
This paper is organized into seven sections. §2 reviews the literature. §3 describes the methodology. §4 presents the empirical results. §5 synthesizes the integrated discussion, principal conclusions, and six targeted policy recommendations. §6 documents the data sources. §7 provides the APA reference list and how-to-cite information.
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