The island is 400 km in the North-South extent and 250 Km in the East-West extent with a mountain massif located in the south-central region which rises to 2.5 Km (Figure 1). The climate in most of Sri Lanka is suitable for malaria transmission. The mean annual temperature is 27°C with a diurnal range of 6°C on average. Rainfall ranges from 500 to 5,500 mm annually, and relative humidity ranges are above 65%, except in a few regions.

The mean annual cycle of rainfall in Sri Lanka is bimodal with a major mode from October to December, and a subsidiary mode from April to June. The rainfall peaks coincide with the passage of the "Tropical Convergence Zone" over the island around May and October. Rainfall during these seasons is high through most of the island. During other periods there is regional variability primarily due to orographic and cyclonic influences. Storms and cyclones affect the rainfall along the north-eastern coast from November to January. The rainfall in the Western region is enhanced from April to October due to topographically enhanced rainfall on the windward side of this mountain ridge, and enhanced on the Eastern side from November to February. ENSO and Indian Ocean sea surfaces are the major factors that influence the inter-annual variability of Sri Lanka's climate system.

The long history (Figure 2) of malaria control in Sri Lanka 2021 offers a number of lessons for current malaria control services both in Sri Lanka and elsewhere. From 1868 to 1948 there have been 20 epidemics in Sri Lanka with case burdens reaching three million (60% of population) in the 1934–35 epidemic 22. Sri Lanka was remarkably successful during the "Global Malaria Eradication Campaign" reducing its burden from a typical two million cases per year in 1948 to just 17 cases in 1963 2324. These control efforts included DDT-spraying 24 and environmental measures such as anti-larval flushing of rivers 2526. It appears that relaxation of control efforts around this time led to rapid and dramatic resurgence reaching 537,700 registered cases in 1969. Following this major epidemic, malaria control was revitalized and case numbers were brought down markedly, but another epidemic in 1975 produced 400,700 cases. Thereafter, there have been epidemics in 1983 (127,000), 1987 (680,000 cases), 1991–2 (400,000 cases) and 1999 (290,000 cases). Presently Sri Lanka hosts 20 million people in a land area of 65,000 square kilometres (Figure 1). Plasmodium falciparum, which historically has been of low prevalence in comparison to Plasmodium vivax, has increased from 5% to above 25% of cases over the past few decades and is increasingly becoming resistant to the first-line anti-malarial drug, chloroquine 27, 28. Recently malaria incidence has declined as Sri Lanka's Anti-Malaria Campaign (AMC) has been implementing more effective malaria control activities. Since 2000 the incidence has reduced even further (Figure 2).

Epidemics occur at three to five year intervals coinciding with low rainfall and high temperature 15, which favors the propagation of the vector Anopheles culicifacies sustained by secondary vectors such as Anopheles subpictus and Anopheles annularis 2930. Malaria transmission in Sri Lanka is complex and vector breeding occurs in a range of settings, including: riverbeds, gem mining pits, seepage areas from irrigation reservoirs and canals and animal foot prints in moist regions, all with different ecological dynamics. The development of epidemic foci has been associated with slash and burn cultivation in poorly accessible deep forested areas, abandoned pits from gem mining areas 31 and the migration of non-immune populations to endemic areas under resettlement programmes. These conditions occurred in late 1967 and 1968 prior to the 1969 epidemic.

Annual mortality and morbidity data from 1911 and monthly incidence data from 1961 up to 2003 were obtained from the Anti-Malaria Campaign (AMC), the publications of its predecessors and compiled from archival records.

An epidemic is the occurrence of cases in excess of the number expected in a given place and time 3233. Thus, in non-endemic areas any transmission including a few cases could constitute an epidemic.

The difficulties in using this definition for endemic areas include not knowing the "expected" number of cases. Endemic malaria has cycles of several years which is usually determined by climate and amplified by loss of immunity in periods of low transmission.

Several practical epidemic detection algorithms have been proposed for monthly case data 6. These detection algorithms rely, for example, on increases from the mean by 1–2 standard deviations or prevalence above some thresholds such as the highest quartile. All these methodologies have some shortcomings arising from the imprecision in definition of epidemics and limitations in applicability due the limited availability of data.

Malaria epidemic years before 1900 have been identified in reports of Ceylon's Civil Medical Department 20. The Director of Medical and Sanitary Services was mandated to undertake malaria control in the first half of the 20^th century. These functions were carried out by the Anti-Malaria Campaign in the second half of the 20^th century. These services have undertaken identification of epidemics as an "expert opinion". Vital statistics, which included morbidity, mortality, birth rate and infant mortality rate were used to identify epidemic years between 1901 and 1934 15. The dramatic increase of death and infant mortality rates with a conspicuous fall of the birth rate marked the epidemic years. The collection of blood films for P. falciparum and P. vivax, and total malaria parasite positives can be analysed to identify epidemic years apart from non-epidemic years 34. In previous analysis on El Niño and epidemics, Bouma and Van der Kaay 14 used the identification from the Director of Medical and Sanitary Services 1520 which was confirmed by retrospective review in 1935 22. The epidemic years were identified as 1877, 1880, 1884, 1891, 1906, 1911, 1914, 1919, 1923, 1928/29, 1934/35, 1939/40, 1943 and 1945/46. This list was used in the analysis reported here to facilitate comparison until 1945. The epidemics since 1945 were identified as 1967/69, 1975, 1983, 1986–1988, 1990/92 and 1999/2000 15 by the Anti-Malaria Campaign of Sri Lanka.

The monthly rainfall data for 17 main meteorological stations from January 1869 to December 2003 were obtained from the Sri Lanka Department of Meteorology (Figure 1). An island-wide rainfall index was constructed based on the records averaged over the 17 well-distributed stations and diagnostics were reported 12. This rainfall index was used for further analysis here.

The ENSO state may be represented by the anomalies of sea-surface temperature (SST) from the seasonal average 35. The mean SST anomalies over the eastern equatorial Pacific (5°N – 5°S, 170° – 120°W) are widely used as an ENSO index (NINO 3.4) 36. An "El Niño" is designated when six consecutive five-month-running-averages of SST's stayed above 0.4°C. A "La Niña year" is designated when six consecutive five-month-running-averages of SST's stayed below 0.4°C.

ENSO states had been identified by calendar year in some of the early literature. For example, Quinn et al 37 provided a listing by year of El Niño events and a measure of event intensity (very strong, strong, moderate, weak, and very weak) beginning in 1726. The measures used to define the El Niño and its intensity was primarily based on phenomena along the coast of South America, and was often qualitative. Note that the focus of Quinn et al was on the Southern Hemisphere summer and fall – which refers to the end of the year. Bouma and Van Der Kaay 14 defined the El Niño years following Quinn et al 37 with additions to the record from Rasmussen and Carpenter 38. They take the first year of a multi-year event as significant.

The definition of ENSO used in this analysis is based on current practice which is based on the use of SST estimates going back to 1856 36.

The monthly average rainfall that characteristically prevails during each ENSO phase may be estimated by averaging the rainfall during each of these phases. For example, to estimate the average monthly rainfall during the El Niño phase, the intervals during which the El Niño phase prevails is identified (based on NINO3.4 > 0.4°C). Thereafter, the monthly rainfall values during these intervals alone are used to estimate the average monthly rainfall. Similarly the average monthly rainfall during the Neutral and La Niña phases are estimated based on the rainfall data when the Neutral (-0.4 < NINO3.4 < 0.4 °C) and La Niña (NINO3.4 < -0.4°C) phases prevailed.

The standard error may be estimated as (s/√n) where s is the sample standard deviation and n is the number of samples.

The hypothesis of positive association of epidemics with El Niño is tested with chi-square (χ²) test for its statistical significance.

In a binary classification system, such as the use of El Niño to identify epidemics, sensitivity and specificity are one approach to quantifying the ability of one variable (such as El Niño) to predict the other variable 39. The sensitivity identifies the proportion of true positives that are identified as such by the predictor. The specificity measures the proportion of negatives, which are identified as such.

Correlation analysis was used to identify relationships between the monthly ENSO indices and rainfall using Pearson algorithm 40. The evolution of correlations between variables was captured with windowed running correlations. To assess the sensitivity of the running correlations of various window lengths can be considered considered.

Monthly average morbidity due to malaria was estimated from incidence data from 1960 to 2003 (Figure 3). The incidence of malaria is bimodal with peaks in December-January and in June-July. The peak at the end of the year is more pronounced than that during the middle of the year. The peaks are two months after the peak in rainfall in November and May. This bimodal distribution of malaria seasonality is retained in the 25 districts. The December-January peak dominates in the Northern and Eastern half of the country.

The features that stand out in comparing the climatology during epidemic and non-epidemic years (figure 4a) are a drop in rainfall, during February and September and an increase in rainfall during October.

The composite climatology for El Niño, Neutral and La Niña was constructed (Figure 4b). Generally, during an El Niño event, rainfall increases from October to December and May, and decreases during January to April and July to August 12. The influence of La Niña events is opposite that of El Niño during October to December (decreases) and July to August (increases), but not during January to March (decreases) El Niño and La Niña episodes usually begin in late boreal spring or fall and last for a period ranging up to a couple of years. Note that the modulations of rainfall during El Niño episodes (Figure 4b) and during epidemics (Figure 4a) are similar over the entire record. This similarity supports the existence of a relationship between El Niño and epidemics.

As an exercise in validation of the contemporaneous expert assessment of epidemics by the government health authorities, the available historical morbidity data may be used in an objective scheme to identify epidemics. The longest morbidity data available to us was annual incidence data aggregated for the island from 1910. Epidemics that are pronounced during some months of the year and in some regions are less prominent when annual aggregated national data is considered rather than monthly district-wise data. Thus a scheme based on consolidated annual data may miss out epidemics that was localized in a few months or in a region but should generally pick out the significant epidemics.

A methodology that takes account of the large decadal variation in morbidity in Sri Lanka (Figure 3) is needed and the simplest of these methods identifies unusual rise in a short window. Here, occasions in which the annual incidence exceeded that of the five-year running mean by half of the standard deviation were identified as an "epidemic". This threshold was arrived at empirically as one that was both simple and that would capture a number of epidemics comparable to the expert opinion. The use of short windows has its shortcomings due to possible rapid variations in mean and standard deviations from one window to the next. Allowance also has to be made for the fact that the epidemics often occur across the calendar year (i.e. from November to February – Figure 3). Thus there can be instances where the objective scheme may identify the adjacent year as an epidemic.

The objective scheme picked out 13 out of the 14 epidemics as identified by expert opinion since 1912. The exception was 1919 but this is a year with a relatively high case load. This was an instance which did not reach the requisite threshold due to a high standard deviation. There was another case that were identified as epidemics by the objective scheme (1956/57) which were not identified as so by expert opinion. This case was during the eradication period with low case-loads (144 cases). Thus the expert determination by the Sri Lanka health authorities is well supported by an objective test for epidemics since 1910. The official assessment was used as it provides a longer record of epidemics starting in 1870.

The association of epidemics with El Niño held from 1870 to 1927 at statistically significant levels (Table 1a). The probability of an epidemic during El Niño phase was 44% (seven epidemics from 16 El Niño) compared to 5% (two in 42) during other years (χ² = 14.4, p < 0.001). The five epidemics between 1928 and 1945 did not coincide with El Niño events. Only four out of 11 epidemics from 1928 to 2000 (Table 1b) occurred during El Niño events. From 1928–1980, only one of seven epidemics occurred during El Niño episodes (Table 1c). Three out of four epidemics from 1981 to 2000 coincided with El Niño events (Table 1d) but the number of events are too few for reliable statistics.

The sensitivity and specificity for 1870 to 1927 are 0.78 and 0.81 respectively. However, for the period from 1928 to 1980 the sensitivity drops to 0.14 while specificity is not overly diminished (0.76). The sensitivity picks up to 0.75 and specificity to 0.88 for the 1981 to 2000 period.

The transition from frequent El Niño-epidemic co-occurrences to infrequent co-occurrences took place around the 1930's. This was well before the more effective malaria control programmes including insecticide spraying was instituted in the mid-1940.

The climatology of rainfall during years when there were epidemics was computed (Figure 4a) along with the rainfall climatology during other years. The rainfall during epidemic years shows a drop during February, August and September and a rise during October and November as seen in the figure and previously reported 12. On average, the rainfall anomalies during El Niño events (Figure 4b) resemble that during epidemic years (Figure 4a).

The relationship between ENSO and rainfall can be investigated further using a running correlation between the ENSO index of NINO3.4 and rainfall (Figure 7). This relationship between the October to December period has been largely consistent over the last 130 years and El Niño events have been associated with enhanced rainfall 41. The relationship for the rest of the year (January to September) shows a substantial positive correlation (> 0.34 the 95% confidence level) in the early part of the record that subsides to near-zero after 1920's. The running correlation turns negative after 1970 reaching magnitudes that are significant at the 95% level. This drop is robust with respect to window-size and is also found for the April to September season. This analysis confirms that there have been dramatic changes in the ENSO influence on Sri Lanka over the last 130 years.

The relationships (where El Niño led to enhanced rainfall) were statistically significant at the 90% level for windows that cover the period from 1869 to 1910. Similarly the inverted relationship in the latter part of the record (where El Niño leads to reduced rainfall) is statistically significant at the 90% level for windows that include the years from 1957 to 2000.

Note, that a more detailed analysis of the running windowed correlation between rainfall and ENSO indices for the Western region was previously reported 42 and this report also documents other physical evidence for decadal changes such as changes in atmospheric circulation patterns associated with ENSO and the reports of other researchers who have observed similar phenomenon across the South Asian and Indian Ocean region.