What Hippocrates knew?

“Whoever wishes to investigate medicine properly should proceed thus: in the first place to consider the seasons of the year, and what effects each of them produces for they are not at all alike, but differ much from themselves in regard to their changes. Then the winds, the hot and the cold, especially such as are common to all countries, and then such as are peculiar to each locality. We must also consider the qualities of the waters, for as they differ from one another in taste and weight, so also do they differ much in their qualities.” Hippocrates in his book “On Airs Water and Places” suggested a strong role of regional hydroclimatology in occurrence of diseases. What have we learned? Are we able to predict outbreaks of diseases? Have we included information on diseases in our sophisticated land-surface hydrological models? The aim of this article to highlight how hydrologists can use existing knowledge and technical skill-base to link it with human health for predicting disease outbreaks. Before we begin, it is important to note that there are only twelve studies published in Water Resources Research and Geophysical Research Letters that have presented methodologies integrating traditional epidemiological understanding with hydroclimatological information-in part, showing the recent nature of the research and future opportunities.

What do we know?

Every year over three million people die as a result of infections from water-related diseases (WHO, 2009). We define water-related diseases as those where the disease causing organism has some of its life cycle associated with water- for example, cholera, malaria, Schistosomiasis etc. A considerable attention has been given to the role of water as a medium for occurrence and outbreak of several water-related diseases in epidemiological literature. However, the functional form of the disease causing organisms is generally broad and needs two distinct processes, macro- and micro- environmental process, for survival and growth. Here, macro-environment is defined as the hydrological and climatic processes affecting the organism and micro-environment encompass the biological processes within the organism (Jutla et al., 2010). The challenge remains as to how to quantify, and establish physical linkages between macro-and micro-environmental processes so that the impact of occurrence of an outbreak can be minimized. Epidemiological literature does not provide much information on how macro-environmental processes influence the micro-environment of the cholera bacteria. Part of the reason why epidemiological literature does not shed light on such linkages is the fact that such those studies primarily focus on the disease pathway after the initial outbreak of the diseases and not on the understanding of underlying large scale hydrological or climatic controls and subsequent prediction of the initial outbreaks of the disease is not a focus of this domain of knowledge. Proper identification and quantification of the large scale processes, such as the hydrological and climatological controls on water-related diseases may provide important understanding of the disease dynamics, temporal and spatial variability of outbreaks, and development of appropriate prediction mechanisms that provide an actionable lead-time. In order to successfully develop warning systems for disease outbreaks requires a bridge between epidemiology and hydrology, which we refer to as Hydroepidemiology. It should be noted that this term has been used by Kay and Falconer (2008), but in limited fate-transport type experimentation. We define hydroepidemiology as the study of hydroclimatological processes on outbreak of water-related diseases that encompass aspects of fate-transport of contaminants. The core philosophy of hydroepidemiology-with three key components: Symptoms – Macroenvironmental processes modulating conditions for relevant Causes – the actual macro-environmental processes within the disease causing agent resulting in Effects-disease outbreak in a population. Understanding role macro-environmental processes in modulating the seasonality and variability of the disease outbreak and its impact on human society remains a challenge.

 

What is the future?

Cholera, a dreaded water-related disease, affects more than 100 countries worldwide. The idea here is to identify the symptom beforehand so that we can possible determine the causes and minimize the effects. We know for last 150 years that cholera, a water-related diarrheal disease, spreads from drinking water contaminated with certain bacteria. The life cycle of the causative bacterium of cholera, Vibrio cholerae, is intricately linked to two vastly different spatial and temporal scales of interacting variables, micro- and macro-environmental processes. Despite steady accumulation of detailed knowledge of V. cholerae in these two domains, our ability to adequately predict when and where the next cholera epidemic will strike remains severely limited. The recent outbreak of cholera in Haiti, which had a fatality rate of about 6%, caught the health authorities in North America by surprise. While the Oral Rehydration Solution (ORS) therapy has been successfully used to control cholera fatality rates in Bangladesh (a cholera endemic region with less than 1% fatality), Haitian authorities were not in a position to rapidly implement ORS. Efforts at using vaccines to reduce infection and spread of cholera also remain at an early stage. We recognize the importance of micro-environmental processes in understanding cholera dynamics in order to develop effective vaccines and treatment protocols. It is highly unlikely that cholera or any other water-related disease will ever be eradicated, since the disease causing agents are always present, adapt and survive in the environment. Consequently, such diseases cannot be defeated by medicine alone. Rather, we need a new approach—an early warning system with several months’ lead time—to minimize the impact of this devastating disease by predicting when and where it will occur and initiating effective intervention strategies. The goal of this approach is to identify the macro-scale hydrological and climatological processes with enough temporal and spatial “memory” to allow the development of an advanced early warning system for disease occurrence and outbreaks. Satellite remote sensing provides efficient and reliable information across various scales which was not available a decade ago. In addition, accuracy of remote sensing data products has greatly improved in recent years. Satellite remote sensing data provides reliable estimates of plankton abundance through chlorophyll, which can form the basis of such a prediction model. However, the functional relationship(s) of cholera incidence with chlorophyll and its predictive capabilities are not well understood. In our recent study (Jutla et al., 2013), we showed that two seasonal cholera occurrence in the Bengal Delta can be predicted two to three months in advance with an overall prediction accuracy exceeding 75% by using combinations of satellite-derived chlorophyll and air temperature data. Such high prediction accuracy is achievable because two seasonal peaks of cholera are predicted using two separate models with distinctive macro-environmental processes. We have shown that interannual variability of pre-monsoon cholera outbreaks is intricately linked with coastal plankton through a cascade of hydro-coastal processes. Post-monsoon cholera outbreaks, on the other hand, are related to wide-spread flooding and subsequent breakdown of sanitary conditions (Akanda et al 2011). FDominant macro-environmental processes affecting the two seasonal peaks of cholera in the Bengal Delta. The rest are the surrogate variables that can be used to revisit and revise the modeling strategies for development of the prediction of cholera.

We now present a thinking philosophy of domain specific knowledge. Hydrology is all about long term trends, variations across scales and development of statistical models. Microbiology of microbes primarily deals with short term trends and in depth studies for outbreak of diseases; and based on one or two outbreaks epidemiological studies develop a generalized patterns and understanding of disease outbreaks. A microbiologist’s thinking model starts with the cause and primarily end with the effect. For example, a microbiologist starts cholera model with bacterium and ends by generalizing it to an outbreak of the disease. For a microbiologist, symptoms does not play important role as the hosts in outbreaks of the disease. Their mental models are associated with the cause of the outbreak. For a hydrologist, cholera outbreaks would essentially start with phytoplankton bloom though river flow but a hydrologist would completely ignore the microbiology portion. For them, environment is more important for outbreak of disease since it provides shelter to host. In other words, an intellectual altercation between hydrologist and microbiologist remains as: is the host (bacteria) important? or is the environment (river flow) important? Are these two different questions or a subset of one question as what causes a disease? Our studies on cholera (Jutla et al., 2010, 2011, 2012, 2013; Akanda et al. 2009, 2010, 2011, 2013) shows how inclusion of mathematical strengths of hydrology and biological information from microbiology, would lead to reductionism through inclusion of remote sensing data. This would further aid in developing a simplistic approach to develop predictive models for disease outbreaks.

 

WHO http://www.voanews.com/content/a-13-2005-03-17-voa34-67381152/274768.html

Kay, D., & Falconer, R. (2008). Hydro-epidemiology: The emergence of a research agenda. Environmental Fluid Mechanics, 8(5-6), 451-459.

Akanda, A. S., Jutla, A. S., Alam, M., De Magny, G. C., Siddique, A. K., Sack, R. B., . . . Islam, S. (2011). Hydroclimatic influences on seasonal and spatial cholera transmission cycles: Implications for public health intervention in the Bengal delta. Water Resources Research, 47(5)

Bertuzzo, E., Azaele, S., Maritan, A., Gatto, M., Rodriguez-Iturbe, I., & Rinaldo, A. (2008). On the space-time evolution of a cholera epidemic. Water Resources Research, 44(1)

Bomblies, A., Duchemin, J. -., & Eltahir, E. A. B. (2008). Hydrology of malaria: Model development and application to a sahelian village. Water Resources Research, 44(12)

Gianotti, R. L., Bomblies, A., & Eltahir, E. A. B. (2009). Hydrologie modeling to screen potential environmental management methods for malaria vector control in niger. Water Resources Research, 45(8)

Reis, J., Culver, T. B., McCartney, M., Lautze, J., & Kibret, S. (2011). Water resources implications of integrating malaria control into the operation of an Ethiopian dam. Water Resources Research, 47(9)

Yamana, T. K., & Eltahir, E. A. B. (2011). On the use of satellite-based estimates of rainfall temporal distribution to simulate the potential for malaria transmission in rural Africa. Water Resources Research, 47(2)

Akanda, A. S., Jutla, A. S., & Islam, S. (2009). Dual peak cholera transmission in Bengal delta: A hydroclimatological explanation. Geophysical Research Letters, 36(19)

Bertuzzo, E., Mari, L., Righetto, L., Gatto, M., Casagrandi, R., Blokesch, M., . . . Rinaldo, A. (2011). Prediction of the spatial evolution and effects of control measures for the unfolding Haiti cholera outbreak. Geophysical Research Letters, 38(6)

Bertuzzo, E., Mari, L., Righetto, L., Gatto, M., Casagrandi, R., Rodriguez-Iturbe, I., & Rinaldo, A. (2012). Hydroclimatology of dual-peak annual cholera incidence: Insights from a spatially explicit model. Geophysical Research Letters, 39(5)

Waliser, D. E., Murtugudde, R., Strutton, P., & Li, J. -. (2005). Subseasonal organization of ocean chlorophyll: Prospects for prediction based on the madden-Julian oscillation. Geophysical Research Letters, 32(23), 1-4.

Jones, A. E., & Morse, A. P. (2012). Skill of ENSEMBLES seasonal re-forecasts for malaria prediction in West Africa. Geophysical Research Letters, 39(23)

McHugh, M. J. (2005). Multi-model trends in east African rainfall associated with increased CO2. Geophysical Research Letters, 32(1), 1-4.