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

Abstract

In tropical countries such as Sri Lanka, where leptospirosis—a deadly disease with a high mortality rate—is endemic, prediction is required for public health planning and resource allocation. Routinely collected meteorological data may offer an effective means of making such predictions. This study included monthly leptospirosis and meteorological data from January 2007 to April 2019 from Sri Lanka. Factor analysis was first used with rainfall data to classify districts into meteorological zones. We used a seasonal autoregressive integrated moving average (SARIMA) model for univariate predictions and an autoregressive distributed lag (ARDL) model for multivariable analysis of leptospirosis with monthly average rainfall, temperature, relative humidity (RH), solar radiation (SR), and the number of rainy days/month (RD). Districts were classified into wet (WZ) and dry (DZ) zones, and highlands (HL) based on the factor analysis of rainfall data. The WZ had the highest leptospirosis incidence; there was no difference in the incidence between the DZ and HL. Leptospirosis was fluctuated positively with rainfall, RH and RD, whereas temperature and SR were fluctuated negatively. The best-fitted SARIMA models in the three zones were different from each other. Despite its known association, rainfall was positively significant in the WZ only at lag 5 (P = 0.03) but was negatively associated at lag 2 and 3 (P = 0.04). RD was positively associated for all three zones. Temperature was positively associated at lag 0 for the WZ and HL (P < 0.009) and was negatively associated at lag 1 for the WZ (P = 0.01). There was no association with RH in contrast to previous studies. Based on altitude and rainfall data, meteorological variables could effectively predict the incidence of leptospirosis with different models for different climatic zones. These predictive models could be effectively used in public health planning purposes.

Estimating the burden of leptospirosis: global lessons from Sri Lanka

“…These lapses in notification could be more frequent during non-seasonal and unexpected outbreaks as leptospirosis is mainly associated with monsoon rain. 9 With the challenge of inadequate investigation facilities, physicians are often left with clinical, biochemical and epidemiological parameters to make a diagnosis. For example, whether the patient worked in a paddy farm is a routine question.…”

“… 14 Serogroups are predominantly reservoir specific, and environmental survival depends on diverse factors in different geographical regions. 14 While the seasonal variation in leptospirosis is well known, 9 the differences in leptospirosis by climatic zones within countries need further exploration, especially with the observed macrogeographical changes in leptospirosis.…”

“…Some models were used to predict severe leptospirosis using clinical features, and some identified seasonal variations. 9 15 However, the clinical utility of these models is questionable. When the number of parameters in the model is higher, the models become complex and incomprehensible.…”

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

“…Leptospirosis was first documented in Sri Lanka in 1953, and the first scientific investigation was published in 1959 [ 4 ]. Since then, cases were frequently identified from almost all districts, but with a particularly high incidence in the wet zone of the country [ 5 ]. In 2008, the Epidemiology Unit of Sri Lanka reported 7,423 cases of leptospirosis, the highest number of cases notified to the surveillance system until the last year (2020) ( Figure 1 ).…”

“…Some of its symptoms resemble those of influenza, and many mild cases of leptospirosis could have been missed during the COVID-19 pandemic due to this reason. Unexpected outbreaks such as COVID-19 can draw physicians’ attention towards the outbreak and could easily lead to misdiagnoses of leptospirosis in a setting where clinical and epidemiological parameters play an important role in the diagnosis [ 5 ].…”