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Leptospirosis is a globally important zoonotic disease. The disease is particularly important in tropical and subtropical countries. Infections in humans can be caused by exposure to infected animals or contaminated soil or water, which are suitable for Leptospira. To explore the cluster area, the Global Moran’s I index was calculated for incidences per 100,000 population at the province level during 2012–2018, using the monthly and annual data. The high-risk and low-risk provinces were identified using the local indicators of spatial association (LISA). The risk factors for leptospirosis were evaluated using a generalized linear mixed model (GLMM) with zero-inflation. We also added spatial and temporal correlation terms to take into account the spatial and temporal structures. The Global Moran’s I index showed significant positive values. It did not demonstrate a random distribution throughout the period of study. The high-risk provinces were almost all in the lower north-east and south parts of Thailand. For yearly reported cases, the significant risk factors from the final best-fitted model were population density, elevation, and primary rice arable areas. Interestingly, our study showed that leptospirosis cases were associated with large areas of rice production but were less prevalent in areas of high rice productivity. For monthly reported cases, the model using temperature range was found to be a better fit than using percentage of flooded area. The significant risk factors from the model using temperature range were temporal correlation, average soil moisture, normalized difference vegetation index, and temperature range. Temperature range, which has strongly negative correlation to percentage of flooded area was a significant risk factor for monthly data. Flood exposure controls should be used to reduce the risk of leptospirosis infection. These results could be used to develop a leptospirosis warning system to support public health organizations in Thailand.

In Thailand, pork is one of the most consumed meats nationwide. Pig farming is hence an important business in the country. However, 95% of the farms were considered smallholders raising only 50 pigs or less. With limited budgets and resources, the biosecurity level in these farms is relatively low. Pig movements have been previously identified as a risk factor in the spread of infectious diseases. Therefore, the present study aimed to explicitly analyze the pig movement network structure and assess its vulnerability to the spread of emerging diseases in Thailand. We used official electronic records of nationwide pig movements throughout the year 2021 to construct a directed weighted one-mode network. Degree centrality, degree distribution, connected components, network community, and modularity were measured to explore the network architectures and properties. In this network, 484,483 pig movements were captured. In which, 379,948 (78.42%) were moved toward slaughterhouses and hence excluded from further analyses. From the remaining links, we suggested that the pig movement network in Thailand was vulnerable to the spread of emerging infectious diseases. Within the network, we found a strongly connected component (SCC) connecting 1044 subdistricts (38.6% of the nodes), a giant weakly connected component (GWCC) covering 98.2% of the nodes (2654/2704), and inter-regional communities with overall network modularity of 0.68. The disease may rapidly spread throughout the country. A better understanding of the nationwide pig movement networks is helpful in tailoring control interventions to cope with the newly emerged diseases once introduced.

Background Nipah virus (NiV) is a fatal zoonotic agent that was first identified amongst pig farmers in Malaysia in 1998, in an outbreak that resulted in 105 fatal human cases. That epidemic arose from a chain of infection, initiating from bats to pigs, and which then spilled over from pigs to humans. In Thailand, bat-pig-human communities can be observed across the country, particularly in the central plain. The present study therefore aimed to identify high-risk areas for potential NiV outbreaks and to model how the virus is likely to spread. Multi-criteria decision analysis (MCDA) and weighted linear combination (WLC) were employed to produce the NiV risk map. The map was then overlaid with the nationwide pig movement network to identify the index subdistricts in which NiV may emerge. Subsequently, susceptible-exposed-infectious-removed (SEIR) modeling was used to simulate NiV spread within each subdistrict, and network modeling was used to illustrate how the virus disperses across subdistricts. Results Based on the MCDA and pig movement data, 14 index subdistricts with a high-risk of NiV emergence were identified. We found in our infectious network modeling that the infected subdistricts clustered in, or close to the central plain, within a range of 171 km from the source subdistricts. However, the virus may travel as far as 528.5 km (R0 = 5). Conclusions In conclusion, the risk of NiV dissemination through pig movement networks in Thailand is low but not negligible. The risk areas identified in our study can help the veterinary authority to allocate financial and human resources to where preventive strategies, such as pig farm regionalization, are required and to contain outbreaks in a timely fashion once they occur.