Human infection with P. knowlesi, commonly misidentified as the more benign P. malariae, are widely distributed across Malaysian Borneo and extend to Peninsular Malaysia. Because P. knowlesi replicates every 24 h, rapid diagnosis and prompt effective treatment are essential. In the absence of a specific routine diagnostic test for P. knowlesi malaria, we recommend that patients who reside in or have traveled to Southeast Asia and who have received a "P. malariae" hyperparasitemia diagnosis by microscopy receive intensive management as appropriate for severe falciparum malaria.

Plasmodium knowlesi, a malaria parasite originally thought to be restricted to macaques in Southeast Asia, has recently been recognized as a significant cause of human malaria. Unlike the benign and morphologically similar P. malariae, these parasites can lead to fatal infections. Malaria parasites, including P. knowlesi, have not yet been detected in macaques of the Kapit Division of Malaysian Borneo, where the majority of human knowlesi malaria cases have been reported. In order to extend our understanding of the epidemiology and evolutionary history of P. knowlesi, we examined 108 wild macaques for malaria parasites and sequenced the circumsporozoite protein (csp) gene and mitochondrial (mt) DNA of P. knowlesi isolates derived from macaques and humans. We detected five species of Plasmodium (P. knowlesi, P. inui, P. cynomolgi, P. fieldi and P. coatneyi) in the long-tailed and pig-tailed macaques, and an extremely high prevalence of P. inui and P. knowlesi. Macaques had a higher number of P. knowlesi genotypes per infection than humans, and some diverse alleles of the P. knowlesi csp gene and certain mtDNA haplotypes were shared between both hosts. Analyses of DNA sequence data indicate that there are no mtDNA lineages associated exclusively with either host. Furthermore, our analyses of the mtDNA data reveal that P. knowlesi is derived from an ancestral parasite population that existed prior to human settlement in Southeast Asia, and underwent significant population expansion approximately 30,000–40,000 years ago. Our results indicate that human infections with P. knowlesi are not newly emergent in Southeast Asia and that knowlesi malaria is primarily a zoonosis with wild macaques as the reservoir hosts. However, ongoing ecological changes resulting from deforestation, with an associated increase in the human population, could enable this pathogenic species of Plasmodium to switch to humans as the preferred host.

Background: Human infections with Plasmodium knowlesi, a simian malaria parasite, are more common than previously thought. They have been detected by molecular detection methods in various countries in Southeast Asia, where they were initially diagnosed by microscopy mainly as Plasmodium malariae and at times, as Plasmodium falciparum. There is a paucity of information on the morphology of P. knowlesi parasites and proportion of each erythrocytic stage in naturally acquired human infections. Therefore, detailed descriptions of the morphological characteristics and differential counts of the erythrocytic stages of P. knowlesi parasites in human infections were made, photographs were taken, and morphological features were compared with those of P. malariae and P. falciparum.

Chikungunya fever swept across many South and South-east Asian countries, following extensive outbreaks in the Indian Ocean Islands in 2005. However, molecular epidemiological data to explain the recent spread and evolution of Chikungunya virus (CHIKV) in the Asian region are still limited. This study describes the genetic Characteristics and evolutionary relationships of CHIKV strains that emerged in Sri Lanka and Singapore during 2006-2008. The viruses isolated in Singapore also included those imported from the Maldives (n51), India (n52) and Malaysia (n531). All analysed strains belonged to the East, Central and South African (ECSA) lineage and were evolutionarily more related to Indian than to Indian Ocean Islands strains. Unique genetic characteristics revealed five genetically distinct subpopulations of CHIKV in Sri Lanka and Singapore, which were likely to have emerged through multiple, independent introductions. The evolutionary network based on E1 gene sequences indicated the acquisition of an alanine to valine 226 substitution (E1-A226V) by virus strains of the Indian sublineage as a key evolutionary event that contributed to the transmission and spatial distribution of CHIKV in the region. The E1-A226V substitution was found in 95.7 % (133/139) of analysed isolates in 2008, highlighting the widespread establishment of mutated CHIKV strains in Sri Lanka, Singapore and Malaysia. As the E1-A226V substitution is known to enhance the transmissibility of CHIKV by Aedes albopictus mosquitoes, this observation has important implications for the design of vector control strategies to fight the virus in regions at risk of chikungunya fever.

Background:With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention.Objectives:We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak.Methods:We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore.Results:Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore’s dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response.Conclusions:Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue.Citation:Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369–1375; http://dx.doi.org/10.1289/ehp.1509981