BackgroundIn resource-limited settings where laboratory capacity is limited and response strategy is non-specific, delayed or inappropriate intervention against outbreaks of Norovirus (NoV) are common. Here we report interventions of two norovirus outbreaks, which highlight the importance of evidence-based modeling and assessment to identify infection sources and formulate effective response strategies.MethodsSpatiotemporal scanning, mathematical and random walk modeling predicted the modes of transmission in the two incidents, which were supported by laboratory results and intervention outcomes.ResultsSimulation results indicated that contaminated water was 14 to 500 fold more infectious than infected individuals. Asymptomatic individuals were not effective transmitters. School closure for up to a week still could not contain the outbreak unless the duration was extended to 10 or more days. The total attack rates (TARs) for waterborne NoV outbreaks reported in China (n = 3, median = 4.37) were significantly (p < 0.05) lower than worldwide (n = 14, median = 41.34). The low TARs are likely due to the high number of the affected population.ConclusionsWe found that school closure alone could not contain Norovirus outbreaks. Overlooked personal hygiene may serve as a hotbed for infectious disease transmission. Our results reveal that evidence-based investigations can facilitate timely interventions of Norovirus transmission.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-016-3716-3) contains supplementary material, which is available to authorized users.

Background Novel coronavirus disease 2019 (COVID-19) has become a global pandemic. This study aims to explore the relationship between key natural and social factors and the transmission of COVID-19 in China. Methods This study collected the number of confirmed cases of COVID-19 in 21 provinces and cities in China as of February 28, 2020. Three provinces were included in the sample: Hainan, Guizhou, and Qinghai. The 18 cities included Shanghai, Tianjin and so on. Key natural factors comprised monthly average temperatures in the January and February 2020 and spatial location as determined by longitude and latitude. Social factors were population density, Gross Domestic Product (GDP), number of medical institutions and health practitioners; as well as the per capita values for GDP, medical institutions, and health practitioners. Excel was used to collate the data and draw the temporal and spatial distribution map of the prevalence rate (PR) and the proportion of local infection (PLI). The influencing factors were analyzed by SPSS 21.0 statistical software, and the relationship between the dependent and independent variables was simulated by 11 models. Finally, we choose the exponential model according to the value of R2 and the applicability of the model. Results The temporal and spatial distribution of the PR varies across the 21 provinces and cities identified. The PR generally decreases with distance from Hubei, except in the case of Shenzhen City, where the converse is observed. The results of the exponential model simulation show that the monthly minimum, median, and maximum average temperatures in January and February, and the latitude and population density are significant and thus will affect the PLI. The corresponding values of R2 are 0.297, 0.322, 0.349, 0.290, 0.314, 0.339, 0.344, and 0.301. The effects of other factors were not statistically significant. Conclusions Among the selected key natural and social factors, higher temperatures may decrease the transmission of COVID-19. From this analysis, it is evident that if the temperature decreases by 1℃, the average PLI increases by 0.01. Further, it was established that locations at more northern latitudes had a higher PLI, and population density showed an inverse relationship with PLI.