2 — Literature Review
Manuscript · APA section
Manuscript · Section 2 of 7 · Paper landing
2.1 Introduction
This section reviews the literature relevant to three core domains: (a) the epidemiology and global burden of typhoid fever, with attention to South Asia and Nepal; (b) climate-driven modulation of typhoid transmission through precipitation, temperature, humidity, and flooding; and (c) the application of machine-learning methods to climate-sensitive disease prediction.
2.2 Epidemiology and global burden of typhoid fever
The GBD Typhoid and Paratyphoid Collaborators estimated 10.9 million incident cases of typhoid fever globally in 2017, with approximately 68 % of the burden concentrated in South Asia, followed by sub-Saharan Africa (15 %) and Southeast Asia (12 %) (GBD 2023 Causes of Death Collaborators, 2025). Global mortality in 2017 was estimated at 116,800 deaths (GBD 2023 Causes of Death Collaborators, 2025). Children aged 5–14 years bear the highest age-specific incidence globally (Parry et al., 2002; World Health Organization, 2018). Multidrug-resistant (MDR) typhoid has become endemic in several South Asian countries, and extensively drug-resistant (XDR) strains have been reported from Pakistan and Nepal (Bhatt et al., 2023; World Health Organization, 2018).
Nepal has historically recorded among the highest per-capita typhoid incidence rates in South Asia. Bhatt et al. (2023) found that enteric fever accounts for 5–8 % of all OPD visits during peak monsoon season, with incidence rates in highly endemic Terai districts exceeding 1,000 cases per 100,000 population per year. Hospital-based blood-culture studies confirm the predominance of S. Typhi (≈ 80–90 % of confirmed episodes) (Bhatt et al., 2023; Parry et al., 2002). The HMIS represents the primary national source of systematic district-level typhoid surveillance (Ministry of Health and Population, Government of Nepal, 2023).
2.3 Climate drivers of typhoid transmission
2.3.1 Precipitation
Heavy rainfall generates surface runoff that carries fecal matter into open water sources, shallow wells, and inadequately protected piped systems (Bao et al., 2023; Hashizume et al., 2010). Hashizume et al. (2010) documented a 2–3 week lag between precipitation and diarrheal disease incidence in Dhaka. Bao et al. (2023) identified one-month lagged precipitation as the strongest contemporaneous climate predictor of typhoid in Jiangsu, China.
2.3.2 Temperature
Laboratory studies establish that S. Typhi growth follows a characteristic thermal response curve, optimal at 37 °C, substantially reduced below 15 °C, and inactivated above 60 °C (Crump et al., 2004). Amin et al. (2021) found that a 1 °C increase in mean monthly temperature was associated with a 3.2 % increase in diarrheal disease in Bangladesh. Temperature also regulates mechanical fly-vector populations (Crump et al., 2004).
2.3.3 Humidity
Relative humidity influences pathogen survival in moist environmental matrices and the activity of fly vectors. Humidity operates primarily as a co-factor amplifying precipitation and temperature effects rather than as an independent primary driver (Bao et al., 2023).
2.3.4 Flood events
Flooding amplifies typhoid risk through simultaneous contamination of multiple water sources, displacement of populations into degraded sanitary conditions, disruption of healthcare access, and immune stress from nutritional deprivation (NPDRR Nepal, 2025; World Health Organization, 2018). NDRRMA (2025) documented 267 flood events in 2022 alone, affecting 48 of 77 districts.
2.4 Climate change in Nepal
Nepal is recognized as among the countries most vulnerable to climate change despite contributing < 0.027 % of global emissions (Ministry of Forests and Environment, Government of Nepal, 2021). The IPCC (2022) projects mountain regions will warm at 1.5–2 times the global average rate. The National Adaptation Plan 2021–2050 (Government of Nepal, 2021) identifies the health sector as a Tier 1 priority for climate adaptation, calling specifically for disease early-warning systems integrated with climate monitoring.
2.5 Machine learning in climate-health modeling
ML methods offer demonstrated advantages over traditional parametric regression for climate-health modeling: handling of high-dimensional predictors with multicollinear variables; modeling of non-linear, threshold, and interaction effects; robustness to violations of distributional assumptions; and built-in feature-importance quantification (Breiman, 2001; Chen & Guestrin, 2016; Nsoesie et al., 2014).
Random Forest (Breiman, 2001) constructs ensemble decision trees via bootstrap aggregation; XGBoost (Chen & Guestrin, 2016) uses regularized gradient boosting and has achieved state-of-the-art results on structured-data prediction tasks; MLP is a feedforward neural network capable of approximating any continuous function given sufficient depth (Nsoesie et al., 2014). Bao et al. (2023) demonstrated XGBoost’s superiority over RF, support-vector, and ridge regression for typhoid prediction in China.
2.6 Research gap
No published study has: (a) applied a multi-model ML framework to predict typhoid incidence across all 77 districts of Nepal; (b) integrated HMIS, CHIRPS, ERA5-Land, and DRR Portal datasets within a unified framework; (c) explicitly modeled the lagged climate–disease relationship and autoregressive case dynamics at the district-month scale; or (d) compared Random Forest, XGBoost, and MLP under temporally held-out evaluation in the Nepali context. This manuscript fills each of these gaps.
← Back: 1 — Introduction · → Next: 3 — Methodology