Early detection of malaria in an endemic area: model development.

A malaria epidemic warning system was established in Thailand in 1984 using graphs displaying the median or mean incidence of malaria over the previous five years compiled from malaria surveillance data throughout the country. This reporting mechanism is not timely enough to detect the occurrence of a malaria epidemic which usually occurs at the district level over a short period of time. An alternative method for early detection of a malaria epidemic employing the Poisson model has been proposed. The development of this early malaria epidemic detection model involved 3 steps: model specification, model validation and model testing. The model was based on data collected at the Vector Borne Disease Control Unit (VBDU) Level. The results of model testing reveal the model can detect increasing numbers of cases earlier, one to two weeks prior to reaching their highest peak of transmission. The system was tested using data from Kanchanaburi Province during 2000 to 2001. Results from model testing show the model may be used for monitoring the weekly malaria situation at the district level. The Poisson model was able to detect malaria early in a highly endemic province with a satisfactory level of prediction. As the application is essential for the malaria officers in monitoring of malaria epidemics, this early detection system was introduced into malaria epidemiological work. The model may be helpful in the decision making process, planning and budget allocation for the Malaria Control Program. The software for early malaria detection is currently implemented in several endemic areas throughout Thailand.

Chronicle of malaria epidemics in Thailand, 1980-2000.

The occurrence of malaria epidemics in Thailand was reviewed from the malaria surveillance report of the National Malaria Control Program. The literature review revealed that the four epidemic periods recorded during 1980-2000 almost always occurred in the provinces and districts located along international borders. Malaria epidemics are caused by various factors such as: extensive population movement, multi-drug resistance development, low immune status of the population, lack of knowledge and appropriate personal protection against mosquito biting, and the re-emergence of malaria transmission in low malarious areas. Such factors can lead to changes in the parasite ratio and appearance of malaria epidemics throughout the country. Evidence related to the burden of malaria epidemics was also reviewed to identify causal factors that will be helpful in future research.

Mekong malaria. II. Update of malaria, multi-drug resistance and economic development in the Mekong region of Southeast Asia.

In an expansion of the first Mekong Malaria monograph published in 1999, this second monograph updates the malaria database in the countries comprising the Mekong region of Southeast Asia. The update adds another 3 years' information to cover cumulative data from the 6 Mekong countries (Cambodia, China/Yunnan, Lao PDR, Myanmar, Thailand, Viet Nam) for the six-year period 1999-2001. The objective is to generate a more comprehensive regional perspective in what is a global epicenter of drug resistant falciparum malaria, in order to improve malaria control on a regional basis in the context of social and economic change. The further application of geographical information systems (GIS) to the analysis has underscored the overall asymmetry of disease patterns in the region, with increased emphasis on population mobility in disease spread. Of great importance is the continuing expansion of resistance of P. falciparum to antimalarial drugs in common use and the increasing employment of differing drug combinations as a result. The variation in drug policy among the 6 countries still represents a major obstacle to the institution of region-wide restrictions on drug misuse. An important step forward has been the establishment of 36 sentinel sites throughout the 6 countries, with the objective of standardizing the drug monitoring process; while not all sentinel sites are fully operational yet, the initial implementation has already given encouraging results in relation to disease monitoring. Some decreases in malaria mortality have been recorded. The disease patterns delineated by GIS are particularly instructive when focused on inter-country distribution, which is where more local collaborative effort can be made to rationalize resource utilization and policy development. Placing disease data in the context of socio-economic trends within and between countries serves to further identify the needs and the potential for placing emphasis on resource rationalization on a regional basis. Despite the difficulties, the 6-year time frame represented in this monograph gives confidence that the now well established collaboration is becoming a major factor in improving malaria control on a regional basis and hopefully redressing to a substantial degree the key problem of spread of drug resistance regionally and eventually globally.