Predictability of epidemic malaria under non-stationary conditions with process-based models combining epidemiological updates and climate variability.

BACKGROUND: Previous studies have demonstrated the feasibility of early-warning systems for epidemic malaria informed by climate variability. Whereas modelling approaches typically assume stationary conditions, epidemiological systems are characterized by changes in intervention measures over time, at scales typically longer than inter-epidemic periods. These trends in control efforts preclude simple application of early-warning systems validated by retrospective surveillance data; their effects are also difficult to distinguish from those of climate variability itself. METHODS: Rainfall-driven transmission models for falciparum and vivax malaria are fitted to long-term retrospective surveillance data from four districts in northwest India. Maximum-likelihood estimates (MLEs) of model parameters are obtained for each district via a recently introduced iterated filtering method for partially observed Markov processes. The resulting MLE model is then used to generate simulated yearly forecasts in two different ways, and these forecasts are compared with more recent (out-of-fit) data. In the first approach, initial conditions for generating the predictions are repeatedly updated on a yearly basis, based on the new epidemiological data and the inference method that naturally lends itself to this purpose, given its time-sequential application. In the second approach, the transmission parameters themselves are also updated by refitting the model over a moving window of time. RESULTS: Application of these two approaches to examine the predictability of epidemic malaria in the different districts reveals differences in the effectiveness of intervention for the two parasites, and illustrates how the 'failure' of predictions can be informative to evaluate and quantify the effect of control efforts in the context of climate variability. The first approach performs adequately, and sometimes even better than the second one, when the climate remains the major driver of malaria dynamics, as found for Plasmodium vivax for which an effective clinical intervention is lacking. The second approach offers more skillful forecasts when the dynamics shift over time, as is the case of Plasmodium falciparum in recent years with declining incidence under improved control. CONCLUSIONS: Predictive systems for infectious diseases such as malaria, based on process-based models and climate variables, can be informative and applicable under non-stationary conditions

Population Density, Climate Variables and Poverty Synergistically Structure Spatial Risk in Urban Malaria in India.

BACKGROUND: The world is rapidly becoming urban with the global population living in cities projected to double by 2050. This increase in urbanization poses new challenges for the spread and control of communicable diseases such as malaria. In particular, urban environments create highly heterogeneous socio-economic and environmental conditions that can affect the transmission of vector-borne diseases dependent on human water storage and waste water management. Interestingly India, as opposed to Africa, harbors a mosquito vector, Anopheles stephensi, which thrives in the man-made environments of cities and acts as the vector for both Plasmodium vivax and Plasmodium falciparum, making the malaria problem a truly urban phenomenon. Here we address the role and determinants of within-city spatial heterogeneity in the incidence patterns of vivax malaria, and then draw comparisons with results for falciparum malaria. METHODOLOGY/PRINCIPAL FINDINGS: Statistical analyses and a phenomenological transmission model are applied to an extensive spatio-temporal dataset on cases of Plasmodium vivax in the city of Ahmedabad (Gujarat, India) that spans 12 years monthly at the level of wards. A spatial pattern in malaria incidence is described that is largely stationary in time for this parasite. Malaria risk is then shown to be associated with socioeconomic indicators and environmental parameters, temperature and humidity. In a more dynamical perspective, an Inhomogeneous Markov Chain Model is used to predict vivax malaria risk. Models that account for climate factors, socioeconomic level and population size show the highest predictive skill. A comparison to the transmission dynamics of falciparum malaria reinforces the conclusion that the spatio-temporal patterns of risk are strongly driven by extrinsic factors. CONCLUSION/SIGNIFICANCE: Climate forcing and socio-economic heterogeneity act synergistically at local scales on the population dynamics of urban malaria in this city. The stationarity of malaria risk patterns provides a basis for more targeted intervention, such as vector control, based on transmission 'hotspots'. This is especially relevant for P. vivax, a more resilient parasite than P. falciparum, due to its ability to relapse and the operational shortcomings of delivering a "radical cure"

Assessing the burden of pregnancy-associated malaria under changing transmission settings

BACKGROUND: The clinical presentation of pregnancy-associated malaria, or PAM, depends crucially on the particular epidemiological settings. This can potentially lead to an underestimation of its overall burden on the female population, especially in regions prone to epidemic outbreaks and where malaria transmission is generally low. METHODS: Here, by re-examining historical data, it is demonstrated how excess female mortality can be used to evaluate the burden of PAM. A simple mathematical model is then developed to highlight the contrasting signatures of PAM within the endemicity spectrum and to show how PAM is influenced by the intensity and stability of transmission. RESULTS: Both the data and the model show that maternal malaria has a huge impact on the female population. This is particularly pronounced in low-transmission settings during epidemic outbreaks where excess female mortality/morbidity can by far exceed that of a similar endemic setting. CONCLUSION: The results presented here call for active intervention measures not only in highly endemic regions but also, or in particular, in areas where malaria transmission is low and seasonal

El Niño and health

El Niño Southern Oscillation (ENSO) is a climate event that originates in the Pacific Ocean but has wide-ranging consequences for weather around the world, and is especially associated with droughts and floods. The irregular occurrence of El Niño and La Niña events has implications for public health. On a global scale, the human effect of natural disasters increases during El Niño. The effect of ENSO on cholera risk in Bangladesh, and malaria epidemics in parts of South Asia and South America has been well established. The strongest evidence for an association between ENSO and disease is provided by time-series analysis with data series that include more than one event. Evidence for ENSO's effect on other mosquito-borne and rodent-borne diseases is weaker than that for malaria and cholera. Health planners are used to dealing with spatial risk concepts but have little experience with temporal risk management. ENSO and seasonal climate forecasts might offer the opportunity to target scarce resources for epidemic control and disaster preparedness

Climate forcing and desert malaria: the effect of irrigation

BACKGROUND: Rainfall variability and associated remote sensing indices for vegetation are central to the development of early warning systems for epidemic malaria in arid regions. The considerable change in land-use practices resulting from increasing irrigation in recent decades raises important questions on concomitant change in malaria dynamics and its coupling to climate forcing. Here, the consequences of irrigation level for malaria epidemics are addressed with extensive time series data for confirmed Plasmodium falciparum monthly cases, spanning over two decades for five districts in north-west India. The work specifically focuses on the response of malaria epidemics to rainfall forcing and how this response is affected by increasing irrigation. METHODS AND FINDINGS: Remote sensing data for the Normalized Difference Vegetation Index (NDVI) are used as an integrated measure of rainfall to examine correlation maps within the districts and at regional scales. The analyses specifically address whether irrigation has decreased the coupling between malaria incidence and climate variability, and whether this reflects (1) a breakdown of NDVI as a useful indicator of risk, (2) a weakening of rainfall forcing and a concomitant decrease in epidemic risk, or (3) an increase in the control of malaria transmission. The predictive power of NDVI is compared against that of rainfall, using simple linear models and wavelet analysis to study the association of NDVI and malaria variability in the time and in the frequency domain respectively. CONCLUSIONS: The results show that irrigation dampens the influence of climate forcing on the magnitude and frequency of malaria epidemics and, therefore, reduces their predictability. At low irrigation levels, this decoupling reflects a breakdown of local but not regional NDVI as an indicator of rainfall forcing. At higher levels of irrigation, the weakened role of climate variability may be compounded by increased levels of control; nevertheless this leads to no significant decrease in the actual risk of disease. This implies that irrigation can lead to more endemic conditions for malaria, creating the potential for unexpectedly large epidemics in response to excess rainfall if these climatic events coincide with a relaxation of control over time. The implications of our findings for control policies of epidemic malaria in arid regions are discussed

Bringing the lab to the field: a new lowland Microparmarion semi-slug (Gastropoda: Ariophantidae) described and DNA-barcoded in the forest

Cybertaxonomy and portable DNA sequencing now make it possible for citizen scientists to be engaged in the discovery and description of new taxa. We here provide a proof of principle. A new semi-slug, Microparmarion exquadratus n. sp. (Ariophantidae) was discovered during a field course in tropical biology in Borneo for citizen scientists. The new species is the first lowland representative of its genus. It differs from other (high-elevation) Microparmarion species by its size, pigmentation on head and tail, and shape of dart sac and receptaculum. As part of the course programme, the participants prepared a taxonomic description and illustrations, and used the mobile genomic laboratory of the course to obtain DNA barcodes. As far as we are aware, this is the first time a new invertebrate species has been described both morphologically and genetically from the fiel

Inapparent infections and cholera dynamics

In many infectious diseases, an unknown fraction of infections produce symptoms mild enough to go unrecorded, a fact that can seriously compromise the interpretation of epidemiological records. This is true for cholera, a pandemic bacterial disease, where estimates of the ratio of asymptomatic to symptomatic infections have ranged from 3 to 100 (refs 1-5). In the absence of direct evidence, understanding of fundamental aspects of cholera transmission, immunology and control has been based on assumptions about this ratio and about the immunological consequences of inapparent infections. Here we show that a model incorporating high asymptomatic ratio and rapidly waning immunity, with infection both from human and environmental sources, explains 50 yr of mortality data from 26 districts of Bengal, the pathogen's endemic home. We find that the asymptomatic ratio in cholera is far higher than had been previously supposed and that the immunity derived from mild infections wanes much more rapidly than earlier analyses have indicated. We find, too, that the environmental reservoir(5,6) (free-living pathogen) is directly responsible for relatively few infections but that it may be critical to the disease's endemicity. Our results demonstrate that inapparent infections can hold the key to interpreting the patterns of disease outbreaks. New statistical methods(7), which allow rigorous maximum likelihood inference based on dynamical models incorporating multiple sources and outcomes of infection, seasonality, process noise, hidden variables and measurement error, make it possible to test more precise hypotheses and obtain unexpected results. Our experience suggests that the confrontation of time-series data with mechanistic models is likely to revise our understanding of the ecology of many infectious diseases.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62519/1/nature07084.pd

Blood volume measurement with indocyanine green pulse spectrophotometry: dose and site of dye administration

(1) To determine the optimal administration site and dose of indocyanine green (ICG) for blood volume measurement using pulse spectrophotometry, (2) to assess the variation in repeated blood volume measurements for patients after subarachnoid hemorrhage and (3) to evaluate the safety and efficacy of this technique in patients who were treated for an intracranial aneurysm. Four repeated measurements of blood volume (BV) were performed in random order of bolus dose (10 mg or 25 mg ICG) and venous administration site (peripheral or central) in eight patients admitted for treatment of an intracranial aneurysm. Another five patients with subarachnoid hemorrhage underwent three repeated BV measurements with 25 mg ICG at the same administration site to assess the coefficient of variation. The mean +/- SD in BV was 4.38 +/- 0.88 l (n = 25) and 4.69 +/- 1.11 l (n = 26) for 10 mg and 25 mg ICG, respectively. The mean +/- SD in BV was 4.59 +/- 1.15 l (n = 26) and 4.48 +/- 0.86 l (n = 25) for central and peripheral administration, respectively. No significant difference was found. The coefficient of variance of BV measurement with 25 mg of ICG was 7.5% (95% CI: 3-12%). There is no significant difference between intravenous administration of either 10 or 25 mg ICG, and this can be injected through either a peripheral or central venous catheter. The 7.5% coefficient of variation in BV measurements determines the detectable differences using ICG pulse spectrophotometr

Forcing Versus Feedback: Epidemic Malaria and Monsoon Rains in Northwest India

Malaria epidemics in regions with seasonal windows of transmission can vary greatly in size from year to year. A central question has been whether these interannual cycles are driven by climate, are instead generated by the intrinsic dynamics of the disease, or result from the resonance of these two mechanisms. This corresponds to the more general inverse problem of identifying the respective roles of external forcings vs. internal feedbacks from time series for nonlinear and noisy systems. We propose here a quantitative approach to formally compare rival hypotheses on climate vs. disease dynamics, or external forcings vs. internal feedbacks, that combines dynamical models with recently developed, computational inference methods. The interannual patterns of epidemic malaria are investigated here for desert regions of northwest India, with extensive epidemiological records for Plasmodium falciparum malaria for the past two decades. We formulate a dynamical model of malaria transmission that explicitly incorporates rainfall, and we rely on recent advances on parameter estimation for nonlinear and stochastic dynamical systems based on sequential Monte Carlo methods. Results show a significant effect of rainfall in the inter-annual variability of epidemic malaria that involves a threshold in the disease response. The model exhibits high prediction skill for yearly cases in the malaria transmission season following the monsoonal rains. Consideration of a more complex model with clinical immunity demonstrates the robustness of the findings and suggests a role of infected individuals that lack clinical symptoms as a reservoir for transmission. Our results indicate that the nonlinear dynamics of the disease itself play a role at the seasonal, but not the interannual, time scales. They illustrate the feasibility of forecasting malaria epidemics in desert and semi-arid regions of India based on climate variability. This approach should be applicable to malaria in other locations, to other infectious diseases, and to other nonlinear systems under forcing

Methodological problems and amendments to demonstrate effects of temperature on the epidemiology of malaria. A new perspective on the highland epidemics in Madagascar, 1972-89.

There is a growing consensus that changes in climate will have major consequences for human health through a reduction in the availability of food and an increasing frequency of natural disasters. However, the contribution of higher temperatures to vector-borne diseases, particularly malaria, remains controversial despite the known biological dependence of both vector and pathogen on climate. Misconceptions and inappropriate use of variables and methods have contributed to the controversy. At present there appears to be more support for non-climatic explanations to account for the resurgence of malaria in the African highlands, e.g. the deterioration of malaria control and the development of drug resistance. An attempt is made here to show that dismissing temperature as a driving force in the case of malaria is premature. Using a de-trended time-series of malaria incidence in Madagascar between 1972 and 1989 indicated that a minimum temperature during 2 months at the start of the transmission season can account for most of the variability between years (r2 = 0.66). These months correspond with the months when the human-vector (Anopheles gambiae sensu lato) contact is greatest. The relationship between El Niño Southern Oscillation (ENSO) and temperature (r = 0.79), and ENSO and malaria (r = 0.64), suggests that there might be an increased epidemic risk during post-Niño years in the Madagascar highlands and therefore warrants increased vigilance and extended control efforts in the first half of 2003. This review suggests that the rejection of climate-disease associations in studies so far published may not have used biologically relevant climate parameters. It highlights the importance of identifying more relevant parameters during critical periods of the transmission season in order to aid epidemic forecasting and to assess the potential impact of global warming