Browsing by Subject "Disease Outbreaks"
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Item Short-term forecasting of the COVID-19 outbreak in India(Oxford University Press, 2021-06-05) Mangla, Sherry; Pathak, Ashok Kumar; Arshad, Mohd; Haque, UbydulAs the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states.Item Spatial and temporal patterns of dengue incidence in northeastern Thailand 2006-2016(BioMed Central Ltd., 2019-08-23) Phanitchat, Thipruethai; Zhao, Bingxin; Haque, Ubydul; Pientong, Chamsai; Ekalaksananan, Tipaya; Aromseree, Sirinart; Thaewnongiew, Kesorn; Fustec, Benedicte; Bangs, Michael J.; Alexander, Neal; Overgaard, Hans J.BACKGROUND: Dengue, a viral disease transmitted by Aedes mosquitoes, is an important public health concern throughout Thailand. Climate variables are potential predictors of dengue transmission. Associations between climate variables and dengue have usually been performed on large-scale first-level national administrative divisions, i.e. provinces. Here we analyze data on a finer spatial resolution in one province, which is often more relevant for effective disease control design. The objective of this study was to investigate the effect of seasonal variations, monthly climate variability, and to identify local clusters of symptomatic disease at the sub-district level based on reported dengue cases. METHODS: Data on dengue cases were retrieved from the national communicable disease surveillance system in Thailand. Between 2006 and 2016, 15,167 cases were recorded in 199 sub-districts of Khon Kaen Province, northeastern Thailand. Descriptive analyses included demographic characteristics and temporal patterns of disease and climate variables. The association between monthly disease incidence and climate variations was analyzed at the sub-district level using Bayesian Poisson spatial regression. A hotspot analysis was used to assess the spatial patterns (clustered/dispersed/random) of dengue incidence. RESULTS: Dengue was predominant in the 5-14 year-old age group (51.1%). However, over time, dengue incidence in the older age groups (> 15 years) gradually increased and was the most affected group in 2013. Dengue outbreaks coincide with the rainy season. In the spatial regression model, maximum temperature was associated with higher incidence. The hotspot analysis showed clustering of cases around the urbanized area of Khon Kaen city and in rural areas in the southwestern portion of the province. CONCLUSIONS: There was an increase in the number of reported dengue cases in older age groups over the study period. Dengue incidence was highly seasonal and positively associated with maximum ambient temperature. However, climatic variables did not explain all the spatial variation of dengue in the province. Further analyses are needed to clarify the detailed effects of urbanization and other potential environmental risk factors. These results provide useful information for ongoing prediction modeling and developing of dengue early warning systems to guide vector control operations.Item The Picture of C. difficile epidemic and non-epidemic ribotypes: are there differences in virulence?(2019-12) Vitucci, John C.; Simecka, Jerry W.; Hodge, Lisa M.; Allen, Michael S.; Hurdle, Julian; Sumien, NathalieThe purpose of these studies was to determine if the epidemic ribotype of C. difficile (now Clostridioides difficile) was more virulent than non-epidemic ribotypes, to ascertain whether clinics contribute to community-acquired C. difficile, and to bridge gaps in understanding between C. difficile epidemiology and pathology. Virulence of the epidemic isolates was determined to be greater than non-epidemic isolates within LD50 studies utilizing a hamster model of C. difficile disease. In the epidemic isolates, increased production of toxins A and B, increased spore adherence ability, and increased production of spores when antibiotic treatment was administered were factors that are believed to play a role both in increased virulence and in the ability of the epidemic isolate to persist as epidemic. Our results indicate that primary care clinics have higher frequency of contamination of C. difficile spores than hospitals. The study also revealed that of all the samples positive for C. difficile, approximately 90% contained the genes for toxins A, B or both. Thus, primary care clinics can be a source of C. difficile and contribute to community-acquired C. difficile. However, the epidemic ribotype of C. difficile was not isolated at significantly increased levels compared to non-epidemic ribotypes. This suggests that other factors may contribute to its increased frequency of C. difficile infection diagnosis. Isolates of the epidemic ribotype were found to be more virulent than other non-epidemic isolates in both the hamster and mouse models of CDI. In particular, the epidemic ribotypes of C. difficile had lower LD50 values in hamsters than the non-epidemic isolates. The increased severity of disease was associated with higher levels of toxins A and B, but not the number of organisms recovered. The increased toxin production was observed in both the hamster and mouse models of CDI. In addition, it is believed that increased ability of epidemic isolate spores to adhere to the intestinal epithelium in vitro, and produce more spores when treated with the antibiotic vancomycin in hamsters are also important contributors to the enhanced virulence and prevalence associated with epidemic isolates of C. difficile. This revealed a possible link between C. difficile's epidemiology and pathology, and suggested that this connection can potentially explain how epidemic ribotypes persist as epidemic. Though it is likely that the factors discussed throughout this dissertation play a significant role in the epidemic ribotype's ability to persist as epidemic, it is important to note that there may be other contributing factors, such as those found in the in vivo environment. These other factors should be accounted for during future studies of C. difficile's virulence, as future ribotypes are characterized, and as novel treatments are developed to combat C. difficile infections. Still, it is strongly believed that the findings in this dissertation contribute significantly to understanding why the epidemic ribotype is epidemic, and to exposing virulence characteristics that are contributing to this.