This paper is concerned with the modeling of infectious disease spread in a composite space-time domain under conditions of uncertainty. We focus on stochastic modeling that accounts for basic mechanisms of disease distribution and multisourced in situ uncertainties. Starting from the general formulation of population migration dynamics and the specification of transmission and recovery rates, the model studies the functional formulation of the evolution of the fractions of susceptible-infected-recovered individuals. The suggested approach is capable of: a) modeling population dynamics within and across localities, b) integrating the disease representation (i.e. susceptible-infected-recovered individuals) with observation time series at different geographical locations and other sources of information (e.g. hard and soft data, empirical relationships, secondary information), and c) generating predictions of disease spread and associated parameters in real time, while considering model and observation uncertainties. Key aspects of the proposed approach are illustrated by means of simulations (i.e. synthetic studies), and a real-world application using hand-foot-mouth disease (HFMD) data from China.

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Background: Previous studies have assessed limited cognitive domains with relatively short exposure to air pollutants, and studies in Asia are limited. Objective: This study aims to explore the association between long-term exposure to air pollutants and cognition in community-dwelling older adults. Methods: This four-year prospective cohort study recruited 605 older adults at baseline (2011–2013) and 360 participants remained at four-year follow-up. Global and domain-specific cognition were assessed biennially. Data on PM2.5 (particulate matter ≤ 2.5 μm diameter, 2005–2015), PM10 (1993–2015), and nitrogen dioxide (NO2, 1993–2015) were obtained from Taiwan Environmental Protection Administration (TEPA). Bayesian Maximum Entropy was utilized to estimate the spatiotemporal distribution of levels of these pollutants. Results: Exposure to high-level PM2.5 (>29.98 μg/m3) was associated with an increased risk of global cognitive impairment (adjusted odds ratio = 4.56; β = −0.60). High-level PMcoarse exposure (>26.50 μg/m3) was associated with poor verbal fluency (β = −0.19). High-level PM10 exposure (>51.20 μg/m3) was associated with poor executive function (β = −0.24). Medium-level NO2 exposure (>28.62 ppb) was associated with better verbal fluency (β = 0.12). Co-exposure to high concentrations of PM2.5, PMcoarse or PM10 and high concentration of NO2 were associated with poor verbal fluency (PM2.5 and NO2: β = −0.17; PMcoarse and NO2: β = −0.23; PM10 and NO2: β = −0.21) and poor executive function (PM10 and NO2: β = −0.16). These associations became more evident in women, apolipoprotein ε4 non-carriers, and those with education > 12 years. Conclusion: Long-term exposure to PM2.5 (higher than TEPA guidelines), PM10 (lower than TEPA guidelines) or co-exposure to PMx and NO2 were associated with poor global, verbal fluency, and executive function over 4 years.

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<p>The Taipei Basin is located in the northwestern part of Taiwan. In the past, it faced the problem of ground subsidence due to the over-pumping of the groundwater layer. Later, due to the implementation of control policies, the situation of groundwater over-pumping has greatly improved, but now it is exposed to the risk of soil liquefaction due to the high groundwater level.</p><p>This research mainly trying to do two things. The first one is to establish the MODFLOW model by objective methods. Because the MODFLOW model was often established based on subjective conditions in the past it results that everyone has a different model in the same research area. This study tries to establish a more objective model. The second thing is to use the established model to develop an optimal pumping strategy, hoping to establish a pumping strategy that can minimize the risk of formation subsidence and soil liquefaction. This study includes an economical loss to assist in quantifying risk. The other constraints are well capacity, nonnegative constraint, soil liquefaction groundwater level upper limit and land subsidence water level lower limit. Evaluating the optimal groundwater control strategy by minimizing economical loss through MODFLOW parameterization using Monte-Carlo simulation.</p>