Role of seasonality and rainfall in Vibrio cholerae infections: A time series model for 11 years surveillance data

Mechanisms of cholera transmission via environment in India and Bangladesh: state of the science review

OBJECTIVES

Cholera has a long history in India and Bangladesh, the region where six out of the past seven global pandemics have been seeded. The changing climate and growing population have led to global cholera cases remaining high despite a consistent improvement in the access to clean water and sanitation. We aim to provide a holistic overview of variables influencing environmental cholera transmission within the context of India and Bangladesh, with a focus on the mechanisms by which they act.

CONTENT

We identified 56 relevant texts (Bangladesh n = 40, India n = 7, Other n = 5). The results of the review found that cholera transmission is associated with several socio-economic and environmental factors, each associated variable is suggested to have at least one mediating mechanism. Increases in ambient temperature and coastal sea surface temperature support cholera transmission via increases in plankton and a preference of Vibrio cholerae for warmer waters. Increased rainfall can potentially support or reduce transmission via several mechanisms.

SUMMARY AND OUTLOOK

Common issues in the literature are co-variance of seasonal factors, limited access to high quality cholera data, high research bias towards research in Dhaka and Matlab (Bangladesh). A specific and detailed understanding of the relationship between SST and cholera incidence remains unclear.

Update on Environmental and Host Factors Impacting the Risk of Vibrio cholerae Infection

Vibrio cholerae is the causative agent of cholera, a diarrheal disease that kills tens of thousands of people each year. Cholera is transmitted primarily by the ingestion of drinking water contaminated with fecal matter, and a safe water supply remains out of reach in many areas of the world. In this Review, we discuss host and environmental factors that impact the susceptibility to V. cholerae infection and the severity of disease.

How climate change can affect cholera incidence and prevalence? A systematic review

Although the number of cholera infection decreased universally, climate change can potentially affect both incidence and prevalence rates of disease in endemic regions. There is considerable consistent evidence, explaining the associations between cholera and climatic variables. However, it is essentially required to compare and interpret these relationships globally. The aim of the present study was to carry out a systematic review in order to identify and appraise the literature concerning the relationship between nonanthropogenic climatic variabilities such as extreme weather- and ocean-related variables and cholera infection rates. The systematic literature review of studies was conducted by using determined search terms via four major electronic databases (PubMed, Web of Science, Embase, and Scopus) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. This search focused on published articles in English-language up to December 31, 2018. A total of 43 full-text studies that met our criteria have been identified and included in our analysis. The reviewed studies demonstrated that cholera incidence is highly attributed to climatic variables, especially rainfall, temperature, sea surface temperature (SST) and El Niño Southern Oscillation (ENSO). The association between cholera incidence and climatic variables has been investigated by a variety of data analysis methodologies, most commonly time series analysis, generalized linear model (GLM), regression analysis, and spatial/GIS. The results of this study assist the policy-makers who provide the efforts for planning and prevention actions in the face of changing global climatic variables.

Developing a forecasting model for cholera incidence in Dhaka megacity through time series climate data

Cholera, an acute diarrheal disease spread by lack of hygiene and contaminated water, is a major public health risk in many countries. As cholera is triggered by environmental conditions influenced by climatic variables, establishing a correlation between cholera incidence and climatic variables would provide an opportunity to develop a cholera forecasting model. Considering the auto-regressive nature and the seasonal behavioral patterns of cholera, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was used for time-series analysis during 2000-2013. As both rainfall (r = 0.43) and maximum temperature (r = 0.56) have the strongest influence on the occurrence of cholera incidence, single-variable (SVMs) and multi-variable SARIMA models (MVMs) were developed, compared and tested for evaluating their relationship with cholera incidence. A low relationship was found with relative humidity (r = 0.28), ENSO (r = 0.21) and SOI (r = -0.23). Using SVM for a 1 °C increase in maximum temperature at one-month lead time showed a 7% increase of cholera incidence (p < 0.001). However, MVM (AIC = 15, BIC = 36) showed better performance than SVM (AIC = 21, BIC = 39). An MVM using rainfall and monthly mean daily maximum temperature with a one-month lead time showed a better fit (RMSE = 14.7, MAE = 11) than the MVM with no lead time (RMSE = 16.2, MAE = 13.2) in forecasting. This result will assist in predicting cholera risks and better preparedness for public health management in the future.

Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks

The use of the harmonic regression model is well accepted in the epidemiological and biostatistical communities as a standard procedure to examine seasonal patterns in disease occurrence. While these models may provide good fit to periodic patterns with relatively symmetric rises and falls, for some diseases the incidence fluctuates in a more complex manner. We propose a two-step harmonic regression approach to improve the model fit for data exhibiting sharp seasonal peaks. To capture such specific behavior, we first build a basic model and estimate the seasonal peak. At the second step, we apply an extended model using sine and cosine transform functions. These newly proposed functions mimic a quadratic term in the harmonic regression models and thus allow us to better fit the seasonal spikes. We illustrate the proposed method using actual and simulated data and recommend the new approach to assess seasonality in a broad spectrum of diseases manifesting sharp seasonal peaks.

Seasonal variation and etiologic inferences of childhood pneumonia and diarrhea mortality in India

Control of pneumonia and diarrhea mortality in India requires understanding of their etiologies. We combined time series analysis of seasonality, climate region, and clinical syndromes from 243,000 verbal autopsies in the nationally representative Million Death Study. Pneumonia mortality at 1 month-14 years was greatest in January (Rate ratio (RR) 1.66, 99% CI 1.51-1.82; versus the April minimum). Higher RRs at 1-11 months suggested respiratory syncytial virus (RSV) etiology. India's humid subtropical region experienced a unique summer pneumonia mortality. Diarrhea mortality peaked in July (RR 1.66, 1.48-1.85) and January (RR 1.37, 1.23-1.48), while deaths with fever and bloody diarrhea (indicating enteroinvasive bacterial etiology) showed little seasonality. Combining mortality at ages 1-59 months with prevalence surveys, we estimate 40,600 pneumonia deaths from Streptococcus pneumoniae, 20,700 from RSV, 12,600 from influenza, and 7200 from Haemophilus influenzae type b and 24,700 diarrheal deaths from rotavirus occurred in 2015. Careful mortality studies can elucidate etiologies and inform vaccine introduction.

Decoding and modelling of time series count data using Poisson hidden Markov model and Markov ordinal logistic regression models

Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month for 11-year span at Christian Medical College, Vellore, India. Using Viterbi algorithm, the best estimate of the state sequence was obtained and hence the transition probability matrix. The mean passage time between the states were estimated. The 95% confidence interval for the mean passage time was estimated via Monte Carlo simulation. The three hidden states of the estimated Markov chain are labelled as 'Low', 'Moderate' and 'High' with the mean counts of 1.4, 6.6 and 20.2 and the estimated average duration of stay of 3, 3 and 4 months, respectively. Environmental risk factors were studied using Markov ordinal logistic regression analysis. No significant association was found between disease severity levels and climate components.

From hospitalization records to surveillance: The use of local patient profiles to characterize cholera in Vellore, India

Despite availability of high quality medical records, health care systems often do not have the resources or tools to utilize these data efficiently. Yet, hospital-based, laboratory-confirmed records may pave the way for building reliable surveillance systems capable of monitoring temporal trends of emerging infections. In this communication, we present a new tool to compress and visualize medical records with a local population profile (LPP) approach, which transforms information into statistically comparable patterns. We provide a step-by-step tutorial on how to build, interpret, and expand the use of LPP using hospitalization records of laboratory-confirmed cholera. We abstracted case information from the databases maintained by the Department of Clinical Microbiology at Christian Medical College in Vellore, India. We used a single-year age distribution to construct LPPs for O1, O139, and non O1/O139 serotypes of Vibrio cholerae. Disease counts and hospitalization rates were converted into fitted kernel-based probability densities. We formally compared LPPs with the Kolmogorov-Smirnov test, and created multi-panel visuals to depict temporal trend, age distribution, and hospitalization rates simultaneously. Our first implementation of LPPs revealed information that is typically gathered from surveillance systems such as: i) estimates of the demographic distribution of diseases and identification of a population at risk, ii) changes in the dominant pathogen presence; and iii) trends in disease occurrence. The LPP demonstrated the benefit of increased resolution in pattern detection of disease for different Vibrio cholerae serotypes and two demographic categories by showing patterns and anomalies that would be obscured by traditional methods of analysis and visualization. LPP can be used effectively to compile basic patient information such as age, sex, diagnosis, location, and time into compact visuals. Future development of the proposed approach will allow public health researchers and practitioners to broadly utilize and efficiently compress large volumes of medical records without loss of information.

Molecular Epidemiology of Cholera Outbreaks during the Rainy Season in Mandalay, Myanmar

Cholera, caused by Vibrio cholerae, remains a global threat to public health. In Myanmar, the availability of published information on the occurrence of the disease is scarce. We report here that cholera incidence in Mandalay generally exhibited a single annual peak, with an annual average of 312 patients with severe dehydration over the past 5 years (since 2011) and was closely associated with the rainy season. We analyzed cholera outbreaks, characterized 67 isolates of V. cholerae serogroup O1 in 2015 from patients from Mandalay, and compared them with 22 V. cholerae O1 isolates (12 from Mandalay and 10 from Yangon) in 2014. The isolates carried the classical cholera toxin B subunit (ctxB), the toxin-coregulated pilus A (tcpA) of Haitian type, and repeat sequence transcriptional regulator (rstR) of El Tor type. Two molecular typing methods, pulsed-field gel electrophoresis and multiple-locus variable-number tandem repeat analysis (MLVA), differentiated the 89 isolates into seven pulsotypes and 15 MLVA profiles. Pulsotype Y15 and one MLVA profile (11, 7, 7, 16, 7) were predominantly found in the isolates from cholera outbreaks in Mandalay, 2015. Pulsotypes Y11, Y12, and Y15 with some MLVA profiles were detected in the isolates from two remote areas, Mandalay and Yangon, with temporal changes. These data suggested that cholera spread from the seaside to the inland area in Myanmar.