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Dengue poses a significant and multifaceted public health challenge in Sri Lanka, encompassing both preventive and curative aspects. Accurate dengue incidence forecasting is pivotal for effective surveillance and disease control. To address this, we developed an Autoregressive Integrated Moving Average (ARIMA) model tailored for predicting weekly dengue cases in the Colombo district. The modeling process drew on comprehensive weekly dengue fever data from the Weekly Epidemiological Reports (WER), spanning January 2015 to August 2020. Following rigorous model selection, the ARIMA (2,1,0) model, augmented with an autoregressive component (AR) of order 16, emerged as the best-fitted model. It underwent initial calibration and fine-tuning using data from January 2015 to August 2020, and was validated against independent 2000 data. Selection criteria included parameter significance, the Akaike Information Criterion (AIC), and Schwarz Bayesian Information Criterion (SBIC). Importantly, the residuals of the ARIMA model conformed to the assumptions of randomness, constant variance, and normality affirming its suitability. The forecasts closely matched observed dengue incidence, offering a valuable tool for public health decision-makers. However, an increased percentage error was noted in late 2020, likely attributed to factors including potential underreporting due to COVID-19-related disruptions amid rising dengue cases. This research contributes to the critical task of managing dengue outbreaks and underscores the dynamic challenges posed by external influences on disease surveillance. [ABSTRACT FROM AUTHOR]

Daily rainfall data (1950-2005) from 11 rain gauge stations located in the dry zone of Sri Lanka are analyzed to obtain useful information on the lengths and the starting dates of four critical dry spells in each location. On average 12 dry spells per year is found in all locations. The mean duration of dry spells varied from 15 to 23 days with a mean of 19 days. Similar results are obtained during the period prior to 1960, after 1990 and 1961-1990. Further it revealed that agro ecological region DL1 is the longest dry spell region and Anamaduwa and Minneriya are the longest dry spell locations. The study found that there are no seasonal characters in dry spells. The variability of short dry spells (<20 days) is low and the rate of short dry spells is around eight per year. The long dry spells (≥20 days) showed high variability and its mean rate is four per year. Considering critical dry spells, longer dry spells tend to occur in the later part of the year in Anamaduwa and Anuradhapura while longer spells occurred during the first quarter in Hambantota. In other stations the longest spell occurred during the mid of the year. Lengths of first two critical dry spells are approximately equal except in Minneriya and Polonnaruwa. First critical dry spell started before 1st April in most of the stations while fourth spell occurred after 30th July. Fourth critical spell in certain years continued to the next year specially in DL1 region. [ABSTRACT FROM AUTHOR]