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. 2025 Aug 19;53(1):111.
doi: 10.1186/s41182-025-00741-5.

Spatiotemporal dynamics and associated drivers of COVID-19 incidence in Nepal

Bipin Kumar Acharya  1   2 , Shristi Sharma  3 , Laxman Khanal  4   5 , Pramod Joshi  6 , Meghnath Dhimal  6
Affiliations

Spatiotemporal dynamics and associated drivers of COVID-19 incidence in Nepal

Bipin Kumar Acharya et al. Trop Med Health. .

Abstract

Background: COVID-19 has been a major global health concern, severely impacting Nepal with thousands of cases and deaths. The patterns of COVID-19 incidence in the country may have varied over time during the pandemic, with geographic factors playing different roles in the early, middle, and later phases of transmission.

Methods: We utilized spatial statistics and GeoDetector methods to analyze district-level variations in COVID-19 incidence across Nepal from January 2020 to December 2022 using laboratory confirmed cases of the disease and a range of physical, biological and socioenvironmental explanatory variables. The analysis focused on identifying spatial patterns, hotspots, and key driving factors contributing to the uneven distribution of COVID-19 cases.

Results: We found an uneven distribution of COVID-19 in Nepal, with persistent hotspots in major cities, such as Kathmandu and Pokhara, reaching up to 133 cases per 1000 population. GeoDetector analysis identified the key drivers, including road density (q = 0.59, p < 0.001), ICU bed distribution (q = 0.51, p < 0.001), and population density (q = 0.46, p < 0.001). While natural environmental factors such as temperature, precipitation, and NDVI had low and statistically insignificant independent explanatory power, their interaction with variables such as nighttime light, NDVI, and population density enhanced explanatory strength, highlighting the complex spatial distribution of COVID-19 incidence.

Conclusions: We recommend that the Nepalese government implement more targeted and region-specific interventions to address COVID-19 outbreaks, especially in persistent hotspot areas, such as Kathmandu and other emerging cities.

Keywords: Epidemic hotspots; GeoDetector; Incidence rate; Spatial statistics; Targeted intervention.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Ethical approval is not applicable for this study as this study is based on publicly available secondary data and all the data were aggregated in district and month. No human subject was contacted for this study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart showing the methodological workflow of the study
Fig. 2
Fig. 2
Spatial distribution of 13 selected variables summarized in 77 districts of Nepal. The figure numbers correspond to the codes used for predictor variables in Table 1
Fig. 2
Fig. 2
Spatial distribution of 13 selected variables summarized in 77 districts of Nepal. The figure numbers correspond to the codes used for predictor variables in Table 1
Fig. 3
Fig. 3
Spatially smoothed incidence rate of COVID-19 during a March 1, 2021; b November 1, 2021; c December 31, 2022; d spread rate January 23, 2020–March 1, 2021; e spread rate January 23, 2020–November 1, 2021; and f spread rate January 23, 2020–December 31, 2022
Fig. 4
Fig. 4
Moran’s scatter plots of spatially smoothed incidence rate of COVID-19 during a March 1, 2021; b November 1, 2021; and c December 30, 2022
Fig. 5
Fig. 5
LISA MAP and Global Moran’s I SEB smoothed incidence during a March 1, 2021; b November 1, 2021; and c December 30, 2022
Fig. 6
Fig. 6
Single factor effects of different drivers (labeled as indicated in Table 1) on the spatial distribution of cumulative COVID-19 incidence rate for a March 1, 2021; b November 1, 2021; and, c December 30, 2022
Fig. 7
Fig. 7
Interaction effects of selected drivers (labeled as indicated in Table 1) with the distribution of spatially smoothed COVID-19 cumulative incidence rate of December 30, 2022
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