Paper — APA Citation

Predictive Modeling of Typhoid Incidence in Nepal · Manuscript

Manuscript · Research · Paper landing

Contents

§	Section
1	Introduction — problem, research question, contribution
2	Literature Review — climate sensitivity, Nepal epidemiology, ML in climate-health
3	Methodology — study design, features, train/test split, three-model spec, evaluation
4	Results — predictor structure, model performance, figures
5	Discussion — climate–typhoid nexus, early warning, NAP alignment, limitations
6	Data — HMIS · CHIRPS · ERA5-Land · DRR portal · CBS shapefiles
7	Sources — full APA reference list & how to cite

Abstract

Typhoid fever, caused by Salmonella enterica serovar Typhi, imposes a persistent and disproportionate public-health burden on Nepal. This manuscript presents a tightened, three-model machine-learning framework for predicting district-level typhoid incidence across Nepal’s 77 districts and 108 months (January 2015 – December 2023; N = 7,327 district-month observations). Four open-access national datasets are integrated: HMIS outpatient surveillance, DRR-portal flood records, CHIRPS precipitation, and ERA5-Land temperature and humidity. Random Forest, XGBoost, and a Multilayer Perceptron are trained on a strictly chronological partition (last 12 months held out for testing). XGBoost is presented as the operational model (R² = 0.8614, RMSE = 129.07, MAE = 69.50, MAPE = 44.64 %), selected on lowest MAE, interpretability under partial inputs, and reproducibility under a fixed random seed. All three architectures converge within 0.01 R², indicating that the climate × autoregressive signal — not the model family — sets the predictive ceiling.

Companion thesis

This manuscript is a condensed companion to the Master’s thesis, which extends the framework with a Weighted Ensemble that achieves R² = 0.8675 and provides a full five-chapter scholarly treatment. The thesis is indexed separately under the Thesis publication.