Spatiotemporal dynamics and multi-pathogen etiology of a norovirus GII-associated diarrheal outbreak in Bihar: a geostatistical analysis of localized spatial autocorrelation

Diarrheal disease outbreaks remain a persistent public health challenge in Bihar, India, often driven by complex environmental and socio-spatial determinants. While traditional epidemiological methods describe disease burden, they frequently underestimate the underlying spatial structure of transmission. This study investigates the etiology and spatiotemporal dynamics of an acute diarrheal outbreak in the Jehanabad district, utilizing geostatistical frameworks to objectively discretize high-risk transmission foci. A rapid outbreak investigation was conducted in the West Kako region, Jehanabad, Bihar following a surge in acute gastroenteritis cases in July 2025. Mixed-methods approach integrating active case finding, molecular diagnostics for enteric pathogens, and environmental water quality assessment was employed. To evaluate spatial dependency Global Moran’s I statistics to case coordinates was applied. Subsequently, local spatial autocorrelation was assessed using the Getis-Ord Gi statistic with Inverse Distance Weighting (IDW) to identify statistically significant hot spots ( p < 0.05) of disease intensity within marginalized Mahadalit tolas. A total of 237 cases were identified, with the highest incidence among individuals aged < 20 years (55.2%). Environmental investigations identified microbiological contamination in household-stored drinking water and a household well, whereas no contamination was detected in the sampled PHED water supply source. Together with the observed spatial clustering of cases, these findings suggest localized environmental exposure potentially associated with point-of-use water contamination. Microbiological analysis revealed a multi-pathogen etiology, with Norovirus GII identified in 22.7% of stool samples ( n = 22) and Hepatitis A virus (HAV) detected in 5.6% of blood samples ( n = 18). Additionally, Escherichia coli was detected in 100% of the stool sample ( n = 16), which were not further screened for isolation of any specific strain. They were probably normal gut flora. However, possibility of presence of other common strains of E. coli causing diarrhoea in humans may not be completely ruled out. The geostatistical analysis confirmed significant global spatial clustering (Moran’s I = 0.1161, z = 3.64, p < 0.001). Localized hot spot analysis (Getis-Ord Gi*) identified high-intensity clusters (99% confidence, Bin + 3) specifically within Paswan and Manjhi Tolas. This study documents one of the first reports of Norovirus GII detection among patients investigated during an acute diarrhoeal disease outbreak in rural Bihar, highlighting a potentially evolving etiological profile. The application of localized spatial autocorrelation successfully transformed aggregate surveillance data into precise, statistically robust of disease clustering, supporting hypothesis of environmental exposure and subsequent person to person transmission rather than single confirmed outbreak source. By establishing a replicable geostatistical template for the objective discretization of disease clusters, this research demonstrates that integrating advanced spatial statistics into routine outbreak investigations enhances the precision of public health interventions in resource-constrained settings.