Clinical characteristics of under-five children having pneumonia and diarrhea with (cases) and without metabolic acidosis (controls).

<p>Figures represent n (%), unless specified. OR: odds ratio. CI: confidence interval.</p><p>IQR: inter-quartile range.SD: standard deviation. WHZ: weight for height z score; SpO₂ = transcutaneously measured blood oxygen concentration.</p

Results of logistic regression to explore independent predictors for metabolic acidosis in diarrheal children with pneumonia.

<p>OR: odds ratio. CI: confidence interval.</p

Distribution of antimicrobial susceptibility of <i>Vibrio cholerae</i> among three age stratum.

<p>Distribution of antimicrobial susceptibility of <i>Vibrio cholerae</i> among three age stratum.</p

Comparative and multinomial logistic regression analysis of characteristics and common etiology of diarrhea among well-nourished with overweight and obese, and malnourished individuals aged 5–19 years.

<p>N.B: Well-nourished considered as reference category.</p

Distribution of antimicrobial susceptibility of <i>Shigella</i> among three age stratum.

<p>Distribution of antimicrobial susceptibility of <i>Shigella</i> among three age stratum.</p

Comparative and multinomial logistic regression analysis of characteristics and common etiology of diarrhea among well-nourished with overweight and obese, and malnourished children under 5 years.

<p>N.B: Well-nourished considered as reference category.</p

Comparative and multinomial logistic regression analysis of characteristics and common etiology of diarrhea among well-nourished with overweight and obese, and malnourished individuals aged above 19 years.

<p>N.B: Well-nourished considered as reference category.</p

Schematic representation of the models.

<p>(A) Mechanistic temporal model. The population is divided into three classes, for the susceptible <i>S</i>, infected <i>I</i> and recovered <i>R</i> individuals. The arrows denote rates of flow among these classes. The force of infection λ includes three components: a long-term trend, secondary transmission that depends on the levels of infection in the population, and primary transmission at a constant rate from an environmental reservoir of the pathogen. The transmission coefficient (or rate) β in secondary transmission incorporates seasonality, interannual variation as a function of the ENSO index, and environmental noise. (B) Statistical spatio-temporal model. Districts of the city known as “thanas” are grouped into two main regions (depicted in orange for the core and in blue for the periphery). Thanas within the same group follow the same dynamical rules in terms of transitions between cholera levels or states from one month to the next. Three states are considered and used to discretize the case data: no cholera (0), low cholera (1), or high cholera (2) as indicated by the different color intensity. The probability of transition between states from one month to the next depends on the season, the maximum state of neighboring districts, and the climate covariate (ENSO). For details on the models, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172355#sec004" target="_blank">Methods</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172355#pone.0172355.s008" target="_blank">S1 Text</a>.</p

Deviation from climatological June-August rainfall for (A) five high flooding years, (B) five low flooding years, and (C) 2016.

<p>Units are (mm/d). The cross symbol indicates the location of Dhaka.</p

Hindcasts for the indicated years and forecast for 2016, for the post-monsoon (Aug-Dec) season of cholera.

<p>The distribution of observed cases for this same post-monsoon period for the training data used to fit the models was used to estimate the values of the 50th (the median), 75th and 95th quantiles. These values are used as thresholds to define outbreaks of increasing size: a season that exceeds the median is considered anomalous, one that exceeds the 75% level, is considered a large outbreak, and one that exceeds the 95% level, an extreme outbreak. The average observed cases for each year are shown next with an indication of whether they exceed the threshold (yes, “outbreak”) or not (no, “no outbreak”). The proportion of 1000 simulations that fall above each threshold level is reported as a probability, and a probability > 50% is interpreted as a prediction of an outbreak, specified in the last column.</p