Some of Statistics Canada’s Contributions to Survey Methodology

doi:10.1201/b16597-7

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…Two separate chains and three cores, each consisting of 50,000 iterations and a burn-in period of 12,500 iterations, were run in parallel to achieve a target acceptance rate of 0.2 ( Supplementary Materials, Figure S1 ). Several measures were used to assess convergence, including the standard Gelmal-Rubin procedure [ 25 ] and target acceptance rates [ 26 ]. The median values and credible intervals are shown in the Supplementary Materials, Table S1 .…”

Section: Methodsmentioning

confidence: 99%

Evaluating the Impact of Intervention Strategies on the First Wave and Predicting the Second Wave of COVID-19 in Thailand: A Mathematical Modeling Study

Mahikul

Chotsiri

Ploddi

et al. 2021

Biology

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Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide. This study aimed to assess and predict the incidence of COVID-19 in Thailand, including the preparation and evaluation of intervention strategies. An SEIR (susceptible, exposed, infected, recovered) model was implemented with model parameters estimated using the Bayesian approach. The model’s projections showed that the highest daily reported incidence of COVID-19 would be approximately 140 cases (95% credible interval, CrI: 83–170 cases) by the end of March 2020. After Thailand declared an emergency decree, the numbers of new cases and case fatalities decreased, with no new imported cases. According to the model’s predictions, the incidence would be zero at the end of June if non-pharmaceutical interventions (NPIs) were strictly and widely implemented. These stringent NPIs reduced the effective reproductive number (Rt) to 0.73 per day (95% CrI: 0.53–0.93) during April and May. Sensitivity analysis showed that contact rate, hand washing, and face mask wearing effectiveness were the parameters that most influenced the number of reported daily new cases. Our evaluation shows that Thailand’s intervention strategies have been highly effective in mitigating disease propagation. Continuing with these strict disease prevention behaviors could minimize the risk of a new COVID-19 outbreak in Thailand.

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Section: Methodsmentioning

confidence: 99%

Evaluating the Impact of Intervention Strategies on the First Wave and Predicting the Second Wave of COVID-19 in Thailand: A Mathematical Modeling Study

Mahikul

Chotsiri

Ploddi

et al. 2021

Biology

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show abstract

Robust measurement of (heavy-tailed) risks: Theory and implementation

Schneider

Schweizer

2015

Journal of Economic Dynamics and Control

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Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis

et al. 2019

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Background Melioidosis is an infectious disease that is transmitted mainly through contact with contaminated soil or water, and exhibits marked seasonality in most settings, including Southeast Asia. In this study, we used mathematical modelling to examine the impacts of such demographic changes on melioidosis incidence, and to predict the disease burden in a developing country such as Thailand. Methodology/Principal findings A melioidosis infection model was constructed which included demographic data, diabetes mellitus (DM) prevalence, and melioidosis disease processes. The model was fitted to reported melioidosis incidence in Thailand by age, sex, and geographical area, between 2008 and 2015, using a Bayesian Markov Chain Monte Carlo (MCMC) approach. The model was then used to predict the disease burden and future trends of melioidosis incidence in Thailand. Our model predicted two-fold higher incidence rates of melioidosis compared with national surveillance data from 2015. The estimated incidence rates among males were two-fold greater than those in females. Furthermore, the melioidosis incidence rates in the Northeast region population, and among the transient population, were more than double compared to the non-Northeast region population. The highest incidence rates occurred in males aged 45–59 years old for all regions. The average incidence rate of melioidosis between 2005 and 2035 was predicted to be 11.42 to 12.78 per 100,000 population per year, with a slightly increasing trend. Overall, it was estimated that about half of all cases of melioidosis were symptomatic. In addition, the model suggested a greater susceptibility to melioidosis in diabetic compared with non-diabetic individuals. Conclusions/Significance The increasing trend of melioidosis incidence rates was significantly higher among working-age Northeast and transient populations, males aged ≥45 years old, and diabetic individuals. Targeted intervention strategies, such as health education and awareness raising initiatives, should be implemented on high-risk groups, such as those living in the Northeast region, and the seasonally transient population.

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Some of Statistics Canada’s Contributions to Survey Methodology

Cited by 5 publications

References 25 publications

Evaluating the Impact of Intervention Strategies on the First Wave and Predicting the Second Wave of COVID-19 in Thailand: A Mathematical Modeling Study

Evaluating the Impact of Intervention Strategies on the First Wave and Predicting the Second Wave of COVID-19 in Thailand: A Mathematical Modeling Study

Robust measurement of (heavy-tailed) risks: Theory and implementation

Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis

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