Time series models for prediction of leptospirosis in different climate zones in Sri Lanka

Analysing the outbreaks of leptospirosis after floods in Kerala, India

A growing number of studies have linked the incidence of leptospirosis with the occurrence of flood events. Nevertheless, the interaction between flood and leptospirosis has not been extensively studied to understand the influence of flood attributes in inducing new cases. This study reviews leptospirosis cases in relation to multiple flood occurrences in Kerala, India. Leptospirosis data were obtained for three years: 2017 (non-flood year) and two years with flooding-2018 (heavy flooding) and 2019 (moderate flooding). We considered the severity of flood events using the discharge, duration and extent of each flooding event and compared them with the leptospirosis cases. The distribution of cases regarding flood discharge and duration was assessed through descriptive and spatiotemporal analyses, respectively. Furthermore, cluster analyses and spatial regression were completed to ascertain the relationship between flood extent and the postflood cases. This study found that postflood cases of leptospirosis can be associated with flood events in space and time. The total cases in both 2018 and 2019 increased in the post-flood phase, with the increase in 2018 being more evident. Unlike the 2019 flood, the flood of 2018 is a significant spatial indicator for postflood cases. Our study shows that flooding leads to an increase in leptospirosis cases, and there is stronger evidence for increased leptospirosis cases after a heavy flood event than after a moderate flooding event. Flood duration may be the most important factor in determining the increase in leptospirosis infections.

Seasonal autoregressive integrated moving average (SARIMA) time-series model for milk production forecasting in pasture-based dairy cows in the Andean highlands

Milk production in the Andean highlands is variable over space and time. This variability is related to fluctuating environmental factors such as rainfall season which directly influence the availability of livestock feeding resources. The main aim of this study was to develop a time-series model to forecast milk production in a mountainous geographical area by analysing the dynamics of milk records thorough the year. The study was carried out in the Andean highlands, using time-series models of monthly milk records collected routinely from dairy cows maintained in a controlled experimental farm over a 9-year period (2008-2016). Several statistical forecasting models were compared. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percent Error (MAPE) were used as selection criteria to compare models. A relation between monthly milk records and the season of the year was modelled using seasonal autoregressive integrated moving average (SARIMA) methods to explore temporal redundancy (trends and periodicity). According to white noise residual test (Q = 13.951 and p = 0.052), Akaike Information Criterion and MAE, MAPE, and RMSE values, the SARIMA (1, 0, 0) x (2, 0, 0)12 time-series model resulted slightly better forecasting model compared to others. In conclusion, time-series models were promising, simple and useful tools for producing reasonably reliable forecasts of milk production thorough the year in the Andean highlands. The forecasting potential of the different models were similar and they could be used indistinctly to forecast the milk production seasonal fluctuations. However, the SARIMA model performed the best good predictive capacity minimizing the prediction interval error. Thus, a useful effective strategy has been developed by using time-series models to monitor milk production and alleviate production drops due to seasonal factors in the Andean highlands.

Prediction of leptospirosis outbreaks by hydroclimatic covariates: a comparative study of statistical models

Leptospirosis, the infectious disease caused by a spirochete bacteria, is a major public health problem worldwide. In Argentina, some regions have climatic and geographical characteristics that favor the habitat of bacteria of the Leptospira genus, whose survival strongly depends on climatic factors, enhanced by climate change, which increase the problems associated with people's health. In order to have a method to predict leptospirosis cases, in this paper, five time series forecasting methods are compared: two parametric (autoregressive integrated moving average and an alternative one that allows covariates, ARIMA and ARIMAX, respectively), two nonparametric (Nadaraya-Watson Kernel estimator, one and two kernels versions, NW-1 K and NW-2 K), and one semiparametric (semi-functional partial linear regression, SFPLR) method. For this, the number of cases of leptospirosis registered from 2009 to 2020 in three important cities of northeastern Argentina is used, as well as hydroclimatic covariates related to the presence of cases. According to the obtained results, there is no method that improves considerably the rest and can be recommended as a unique tool for leptospirosis prediction. However, in general, the NW-2 K method gets a better performance. This work, in addition to using a long-term high-quality time series, enriches the area of applications of statistical models to epidemiological leptospirosis data by the incorporation of hydroclimatic variables, and it is recommended directing further efforts in this line of research, under the context of current climate change.

SARIMA and ARDL models for predicting leptospirosis in Anuradhapura district Sri Lanka

Leptospirosis is considered a neglected tropical disease despite its considerable mortality and morbidity. Lack of prediction remains a major reason for underestimating the disease. Although many models have been developed, most of them focused on the districts situated in the wet zone due to higher case numbers in that region. However, leptospirosis remains a major disease even in the dry zone of Sri Lanka. The objective of this study is to develop a time series model to predict leptospirosis in the Anuradhapura district situated in the dry zone of Sri Lanka. Time series data on monthly leptospirosis incidences from January 2008 to December 2018 and monthly rainfall, rainy days, temperature, and relative humidity were considered in model fitting. The first 72 months (55%) were used to fit the model, and the subsequent 60 months(45%) were used to validate the model. The log-transformed dependent variable was employed for fitting the Univariate seasonal ARIMA model. Based on the stationarity of the mean of the five variables, the ARDL model was selected as the multivariate time series technique. Residuals analysis was performed on normality, heteroskedasticity, and serial correlation to validate the model. The lowest AIC and MAPE were used to select the best model. Univariate models could not be fitted without adjusting the outliers. Adjusting seasonal outliers yielded better results than the models without adjustments. Best fitted Univariate model was ARIMA(1,0,0)(0,1,1)₁₂,(AIC-1.08, MAPE-19.8). Best fitted ARDL model was ARDL(1, 3, 2, 1, 0),(AIC-2.04,MAPE-30.4). The number of patients reported in the previous month, rainfall, rainy days, and temperature showed a positive association, while relative humidity was negatively associated with leptospirosis. Multivariate models fitted better than univariate models for the original data. Best-fitted models indicate the necessity of including other explanatory variables such as patient, host, and epidemiological factors to yield better results.

Neglecting the neglected during the COVID-19 pandemic: the case of leptospirosis in Sri Lanka

The coronavirus disease 2019 (COVID-19) pandemic has disrupted global health systems and affected the transmission dynamics as well as the surveillance of other infectious diseases. This study described the probable effect of the COVID-19 pandemic on the surveillance and control of leptospirosis in Sri Lanka. With 8,579 reported cases and more than 800 estimated deaths, the Sri Lankan public health surveillance system documented the largest outbreak of leptospirosis in Sri Lankan history in 2020. This was the worst infectious disease outbreak Sri Lanka experienced in 2020, but it was neglected, primarily due to the COVID-19 pandemic.