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

Open access publication of public health research in African journals

There are many claims to the benefits of open access publishing in general and for Africa in particular. This study aimed to describe the characteristics of scholarly journals expected to publish articles on public health from a number of African countries. Using African Journals Online and African Index Medicus, 174 journals from 13 African countries were identified. The six countries above the group’s median gross domestic product (GDP) published 145 journals, while the seven countries at or below the median GDP published 29 journals. Two thirds of the journals were freely available to download, but only a third had a Creative Commons licence, and most were not indexed. Around half of the journals levied full article processing charges (APCs) – journals from countries at median GDP or below were less likely to charge APCs than those from countries above the median GDP. One of the key findings is that only a few journals were indexed, limiting the ability of potential readers to find the results of research performed in local settings. The results suggest a need to assist journals and researchers to make the work they publish more accessible to the audience who might want to use the 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

Estimating the burden of leptospirosis in Sri Lanka; a systematic review

Abstract Background Although the assessment of disease burden should be a priority for allocating resources, leptospirosis is grossly underestimated despite its true burden in Sri Lanka. This study aimed to assess the morbidity and mortality of leptospirosis based on routine surveillance data, hospital reported data and scientific publications from Sri Lanka. Method A systematic review was carried out, and Pub Med, MEDLINE®, BIOSIS Previews, Zoological Record, Web of Science Core Collection, Current Contents Connect, KCI-Korean Journal Database, BIOSIS Citation Index, Data Citation Index, SciELO Citation Index and Google Scholar databases were searched. Quarterly epidemiological bulletin (QEB), indoor morbidity & mortality returns (IMMR) and hand searches of local literature were performed in local libraries. Forty-two relevant full texts, 32 QEBs, and 8 IMMR were included in the full text review. Adjustments were made for under diagnosis, underreporting and chance variability. Results The estimated annual caseload of leptospirosis in Sri Lanka from 2008 to 2015, was 10,423, and the cumulative annual incidence of leptospirosis that required hospitalization was 52.1 (95% CI 51.7–52.6) per 100,000 people. The estimated number of annual deaths due to leptospirosis was approximately 730 (95% CI 542–980), with an estimated pooled case fatality ratio of 7.0% (95% CI 5.2–9.4). The most common organs involved were the kidney, liver and heart, with median rates of 48.7, 30, and 14.2%, respectively. Conclusion Our systematic review shows gross underestimation of the true leptospirosis burden in the national statistics of Sri Lanka, and the hospitalization rates estimated in our study were compatible with the total burden estimate of 300·6 (95% CI 96·54–604·23) per 100,000 people published previously

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

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

Whole genome sequencing data of Leptospira weilii and Leptospira kirschneri isolated from human subjects of Sri Lanka

Leptospirosis is a re-emerging zoonotic disease. This article reports the complete genome sequences of three novel strains of Genus Leptospira: two from the species Leptospira weilii (FMAS_RT1, FMAS_PD2) and one from Leptospira kirschneri (FMAS_PN5). These isolates were recovered from the blood samples of acute febrile patients in different geographical and climatic zones of Sri Lanka. High-quality genomic DNA was extracted from the three isolates in mid-log phase cultures. Whole genome sequencing was conducted using the PacBio Single Molecule Real-Time (SMRT) platform to identify the species, genome features, and novelty of the strains. The annotation was conducted using RAST (Rapid Annotation Using Subsystem Technology version 2.0) and the NCBI Prokaryotic Genome Annotation Pipeline. The genome sequences of three isolates have been deposited in the Mendeley data repository and the National Center for Biotechnology Information (NCBI) repository. This data will be useful for future researchers when conducting comparative genomic analysis, revealing the exact mechanism of pathogenesis of leptospirosis and developing molecular diagnostic tools for early detection