Research Article: Assessing the performance of remotely-sensed flooding indicators and their potential contribution to early warning for leptospirosis in Cambodia

Date Published: July 13, 2017

Publisher: Public Library of Science

Author(s): Julia Ledien, Sopheak Sorn, Sopheak Hem, Rekol Huy, Philippe Buchy, Arnaud Tarantola, Julien Cappelle, Guy J-P. Schumann.


Remote sensing can contribute to early warning for diseases with environmental drivers, such as flooding for leptospirosis. In this study we assessed whether and which remotely-sensed flooding indicator could be used in Cambodia to study any disease for which flooding has already been identified as an important driver, using leptospirosis as a case study. The performance of six potential flooding indicators was assessed by ground truthing. The Modified Normalized Difference Water Index (MNDWI) was used to estimate the Risk Ratio (RR) of being infected by leptospirosis when exposed to floods it detected, in particular during the rainy season. Chi-square tests were also calculated. Another variable—the time elapsed since the first flooding of the year—was created using MNDWI values and was also included as explanatory variable in a generalized linear model (GLM) and in a boosted regression tree model (BRT) of leptospirosis infections, along with other explanatory variables. Interestingly, MNDWI thresholds for both detecting water and predicting the risk of leptospirosis seroconversion were independently evaluated at -0.3. Value of MNDWI greater than -0.3 was significantly related to leptospirosis infection (RR = 1.61 [1.10–1.52]; χ2 = 5.64, p-value = 0.02, especially during the rainy season (RR = 2.03 [1.25–3.28]; χ2 = 8.15, p-value = 0.004). Time since the first flooding of the year was a significant risk factor in our GLM model (p-value = 0.042). These results suggest that MNDWI may be useful as a risk indicator in an early warning remote sensing tool for flood-driven diseases like leptospirosis in South East Asia.

Partial Text

Remote sensing provides a large variety of indicators that can inform Public Health especially when diseases have environmental drivers [1]. Remotely-sensed environmental indicators can help to understand the epidemiology of such diseases, predict health risks and improve timely and targeted response to outbreaks. Models using remote sensing data can for example be used as an early warning tool when changes in environmental indicators have been shown to predict an outbreak [2,3]. After the launch of Landsat 1 in the 70’s and the development of earth observation systems in the 80’s and the 90’s, several environmental indicators produced from satellite images were used for health studies, especially for the surveillance of vector-borne diseases which usually have strong environmental drivers [4]. Remotely-sensed data were used to map different factors such as vegetation, deforestation, flooding or urban features and infer the associated risk of disease, promising the development of early warning systems for major health threats such as Rift Valley Fever or malaria [3–7].

In this study—the first of its kind to our knowledge in Cambodia—we (i) assessed the performance of six different remotely-sensed flooding indicators and (ii) assessed whether the most effective one could be used in predicting the distribution of human leptospirosis infections at local level in Kampong Cham province, Cambodia. MNDWI was the best flooding indicator based on field observations. Interestingly, the threshold of -0.3 maximizing the performance of MNDWI as a flooding indicator was also the optimal threshold to discriminate leptospirosis infection according to both our sensitivity-specificity evaluation methods and the BRT model. Two independent analyses based on independent datasets showed that MNDWI values greater than -0.3 were significantly associated with both flooding and an increased risk of leptospirosis infection even during the rainy season (RR = 2.03 [1.25–3.28], chi-square = 8.15, p-value = 0.004). This is consistent with what is known of leptospirosis epidemiology and the role of flooding in leptospirosis outbreaks [13,18,20,31,45–47] but differs from the results of Suwanpakdee et al. which did not find a direct correlation between leptospirosis cases and flooding in Thailand [48]. However, they used confirmed and suspected leptospirosis cases reported to the National surveillance system—that may lead to an important under-detection of the real number of cases- whereas we used confirmed cases from a community active surveillance of undifferentiated fevers [23,48]. Our results illustrate the potential for this flooding indicator to be used as an early warning predictor of an increased leptospirosis risk in Cambodia and probably in other countries for leptospirosis or for other diseases strongly associated with flooding.




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