Application of satellite precipitation data to analyse and model arbovirus activity in the tropics

Weekly Forecasting of Yellow Fever Occurrence and Incidence via Eco-Meteorological Dynamics

Yellow Fever (YF), a mosquito-borne disease, requires ongoing surveillance and prevention due to its persistence and ability to cause major epidemics, including one that began in Brazil in 2016. Forecasting based on factors influencing YF risk can improve efficiency in prevention. This study aimed to produce weekly forecasts of YF occurrence and incidence in Brazil using weekly meteorological and ecohydrological conditions. Occurrence was forecast as the probability of observing any cases, and incidence was forecast to represent morbidity if YF occurs. We fit gamma hurdle models, selecting predictors from several meteorological and ecohydrological factors, based on forecast accuracy defined by receiver operator characteristic curves and mean absolute error. We fit separate models for data before and after the start of the 2016 outbreak, forecasting occurrence and incidence for all municipalities of Brazil weekly. Different predictor sets were found to produce most accurate forecasts in each time period, and forecast accuracy was high for both time periods. Temperature, precipitation, and previous YF burden were most influential predictors among models. Minimum, maximum, mean, and range of weekly temperature, precipitation, and humidity contributed to forecasts, with optimal lag times of 2, 6, and 7 weeks depending on time period. Results from this study show the use of environmental predictors in providing regular forecasts of YF burden and producing nationwide forecasts. Weekly forecasts, which can be produced using the forecast model developed in this study, are beneficial for informing immediate preparedness measures.

Evaluation of Eight High-Resolution Gridded Precipitation Products in the Heihe River Basin, Northwest China

The acquisition of the precise spatial distribution of precipitation is of great importance and necessity in many fields, such as hydrology, meteorology and ecological environments. However, in the arid and semiarid regions of Northwest China, especially over mountainous areas such as the Heihe River basin (HRB), the scarcity and uneven distribution of rainfall stations have created certain challenges in gathering information that accurately describes the spatial distribution of precipitation for use in applications. In this study, the accuracy of precipitation estimates from eight high-resolution gridded precipitation products (CMORPHv1-CRT, CRU TSv.4.05, ERA5, GSMaP_NRT, IMERG V06B-Final, MSWEPv2.0, PERSIANN-CDR and TRMM 3B42v7) are comprehensively evaluated by referring to the precipitation observations from 23 stations over the HRB using six indices (root mean square error, standard deviation, Pearson correlation coefficient, relative deviation, mean error and Kling–Gupta efficiency) from different spatial and temporal scales. The results show that at an annual scale, MSWEP has the highest accuracy over the entire basin, while PERSIANN, CRU and ERA5 show the most accurate results in the upper, middle and lower reaches of the HRB, respectively. At a seasonal scale, the performance of IMERG, CRU and ERA5 is superior to that of the other products in all seasons in the upper, middle and lower reaches, respectively. Over the entire HRB, PERSIANN displays the smallest deviation in all seasons except for spring. TRMM shows the highest accuracy in spring and autumn, while MSWEP and CRU show the highest accuracy in summer and winter, respectively. At a monthly scale, TRMM is superior to the other products, with a relatively stronger correlation almost every month, while GSMaP is inferior to the other products. Moreover, MSWEP and PERSIANN perform relatively best, with favorable statistical results around almost every station, while GSMaP shows the worse performance. In addition, ERA5 tends to overestimate higher values, while GSMaP tends to overestimate lower values over the entire basin. Moreover, the overestimation of ERA5 tends to appear in the upper reach area, while that of GSMaP tends to appear in the lower reach area. Only CRU and PERSIANN yield underestimations of precipitation, with the strongest tendency appearing in the upper reach area. The results of this study display some findings on the uncertainties of several frequently used precipitation datasets in the high mountains and poorly gauged regions in the HRB and will be helpful to researchers in various fields who need high-resolution gridded precipitation datasets over the HRB, as well as to data producers who want to improve their products.

Evaluation of Satellite Precipitation Products in Simulating Streamflow in a Humid Tropical Catchment of India Using a Semi-Distributed Hydrological Model

Precipitation obtained from rain gauges is an essential input for hydrological modelling. It is often sparse in highly topographically varying terrain, exhibiting a certain amount of uncertainty in hydrological modelling. Hence, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. In this study, an attempt was made to evaluate the Tropical Rainfall Measuring Mission (TRMM) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), employing a semi-distributed hydrological model, i.e., Soil and Water Assessment Tool (SWAT), for simulating streamflow and validating them against the flows generated by the India Meteorological Department (IMD) rainfall dataset in the Gurupura river catchment of India. Distinct testing scenarios for simulating streamflow were made to check the suitability of these satellite precipitation data. The TRMM was able to better estimate rainfall than CHIRPS after performing categorical and continuous statistical results with respect to IMD rainfall data. While comparing the performance of model simulations, the IMD rainfall-driven streamflow emerged as the best followed by the TRMM, CHIRPS-0.05, and CHIRPS-0.25. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) were in the range 0.63 to 0.86, 0.62 to 0.86, and −14.98 to 0.87, respectively. Further, an attempt was made to examine the spatial distribution of key hydrological signature, i.e., flow duration curve (FDC) in the 30–95 percentile range of non-exceedance probability. It was observed that TRMM underestimated the flow for agricultural water availability corresponding to 30 percent, even though it showed a good performance compared to the other satellite rainfall-driven model outputs.

Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

Applications of Space Technologies to Global Health: Scoping Review

Background

Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations’ Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health.

Objective

The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges.

Methods

We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs’ Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified.

Results

A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use.

Conclusions

Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.

A Bayesian Belief Network for Murray Valley encephalitis virus risk assessment in Western Australia

BACKGROUND

Murray Valley encephalitis virus (MVEV) is a clinically important virus in Australia responsible for a number of epidemics over the past century. Since there is no vaccine for MVEV, other preventive health measures to curtail its spread must be considered, including the development of predictive risk models and maps to help direct public health interventions. This article aims to support these approaches by presenting a model for assessing MVEV risk in Western Australia (WA).

METHODS

A Bayesian Belief Network (BBN) for assessing MVEV risk was developed and used to quantify and map disease risks in WA. The model combined various abiotic, biotic, and anthropogenic factors that might affect the risk of MVEV into a predictive framework, based on the ecology of the major mosquito vector and waterbird hosts of MVEV. It was further refined and tested using retrospective climate data from 4 years (2000, 2003, 2009, and 2011).

RESULTS

Implementing the model across WA demonstrated that it could predict locations of human MVEV infection and sentinel animal seroconversion in the 4 years tested with some degree of accuracy. In general, risks are highest in the State's north and lower in the south. The model predicted that short-term climate change, based on the Intergovernmental Panel on Climate Change's A1B emissions scenario, would decrease MVEV risks in summer and autumn, largely due to higher temperatures decreasing vector survival.

CONCLUSIONS

To our knowledge, this is the first model to use a BBN to quantify MVEV risks in WA. The models and maps developed here may assist public health agencies in preparing for and managing Murray Valley encephalitis in the future. In its current form, the model is knowledge-driven and based on the analysis of potential risk factors that affect the dynamics of MVEV using retrospective data. Further work and additional testing should be carried out to test its validity in future years.

Spatio-Temporal Identification of Areas Suitable for West Nile Disease in the Mediterranean Basin and Central Europe

West Nile virus (WNV) is a mosquito-transmitted Flavivirus belonging to the Japanese encephalitis antigenic complex of the Flaviviridae family. Its spread in the Mediterranean basin and the Balkans poses a significant risk to human health and forces public health officials to constantly monitor the virus transmission to ensure prompt application of preventive measures. In this context, predictive tools indicating the areas and periods at major risk of WNV transmission are of paramount importance. Spatial analysis approaches, which use environmental and climatic variables to find suitable habitats for WNV spread, can enhance predictive techniques. Using the Mahalanobis Distance statistic, areas ecologically most suitable for sustaining WNV transmission were identified in the Mediterranean basin and Central Europe. About 270 human and equine clinical cases notified in Italy, Greece, Portugal, Morocco, and Tunisia, between 2008 and 2012, have been considered. The environmental variables included in the model were altitude, slope, night time Land Surface Temperature, Normalized Difference Vegetation Index, Enhanced Vegetation Index, and daily temperature range. Seasonality of mosquito population has been modelled and included in the analyses to produce monthly maps of suitable areas for West Nile Disease. Between May and July, the most suitable areas are located in Tunisia, Libya, Egypt, and North Cyprus. Summer/Autumn months, particularly between August and October, characterize the suitability in Italy, France, Spain, the Balkan countries, Morocco, North Tunisia, the Mediterranean coast of Africa, and the Middle East. The persistence of suitable conditions in December is confined to the coastal areas of Morocco, Tunisia, Libya, Egypt, and Israel.

Rainfall and sentinel chicken seroconversions predict human cases of Murray Valley encephalitis in the north of Western Australia

BACKGROUND

Murray Valley encephalitis virus (MVEV) is a flavivirus that occurs in Australia and New Guinea. While clinical cases are uncommon, MVEV can cause severe encephalitis with high mortality. Sentinel chicken surveillance is used at many sites around Australia to provide an early warning system for risk of human infection in areas that have low population density and geographical remoteness. MVEV in Western Australia occurs in areas of low population density and geographical remoteness, resulting in logistical challenges with surveillance systems and few human cases. While epidemiological data has suggested an association between rainfall and MVEV activity in outbreak years, it has not been quantified, and the association between rainfall and sporadic cases is less clear. In this study we analysed 22 years of sentinel chicken and human case data from Western Australia in order to evaluate the effectiveness of sentinel chicken surveillance for MVEV and assess the association between rainfall and MVEV activity.

METHODS

Sentinel chicken seroconversion, human case and rainfall data from the Kimberley and Pilbara regions of Western Australia from 1990 to 2011 were analysed using negative binomial regression. Sentinel chicken seroconversion and human cases were used as dependent variables in the model. The model was then tested against sentinel chicken and rainfall data from 2012 and 2013.

RESULTS

Sentinel chicken seroconversion preceded all human cases except two in March 1993. Rainfall in the prior three months was significantly associated with both sentinel chicken seroconversion and human cases across the regions of interest. Sentinel chicken seroconversion was also predictive of human cases in the models. The model predicted sentinel chicken seroconversion in the Kimberley but not in the Pilbara, where seroconversions early in 2012 were not predicted. The latter may be due to localised MVEV activity in isolated foci at dams, which do not reflect broader virus activity in the region.

CONCLUSIONS

We showed that rainfall and sentinel chickens provide a useful early warning of MVEV risk to humans across endemic and epidemic areas, and that a combination of the two indicators improves the ability to assess MVEV risk and inform risk management measures.

A review of domestic animal diseases within the Pacific Islands region

The Pacific Island countries and territories (PICTs) are reported to be free of the most serious infectious livestock diseases which are prevalent in other parts of the globe, such as Highly Pathogenic Avian Influenza, Foot and Mouth Disease or Rabies. Yet there is a lack of scientifically based evidence to confirm this animal health status. This paper reviews what has been published on diseases of domestic animals in the Pacific Islands region with a particular focus on data from the last 20 years (1992-2012). Relevant published papers were identified by a computerized literature search of two electronic databases (PubMed and Web of Knowledge). The latest reports on the animal health situation submitted by the PICTs to the World Organisation for Animal Health (OIE) were accessed on the World Animal Health Information Database (WAHID) interface and included in this review. Additionally, paper searches of resources were undertaken at the library of the Secretariat of the Pacific Community (SPC) in Fiji to retrieve any relevant grey literature for this review. The study eligibility criteria included qualitative or quantitative information on any disease (bacterial, viral, parasitic and other health disorders) affecting domestic terrestrial animals (mammals, reptiles, birds and bees) in any of the 22 PICTs members of the SPC. A total of 158 eligible references were retrieved of which only 77 (48.7%) were published since 1992 and analysed in more details. One hundred and one diseases and pathogens were reported on for bee, bird, carabao, cat, cattle, crocodile, deer, dog, donkey, goat, horse, pig, pigeon, poultry and sheep in the Oceania region and in 17 PICTs in particular. The paper gives information about known animal diseases, their reported prevalence and diseases not reported within the Pacific Islands region. The study found retrieved literature on animal diseases in PICTs was scarce and no longer up to date. There is a need to improve the published knowledge on the current animal disease status in the region.

The changing epidemiology of Kunjin virus in Australia

West Nile virus (WNV) is a mosquito-borne virus responsible for outbreaks of viral encephalitis in humans and horses, with particularly virulent strains causing recent outbreaks of disease in Eastern Europe, the Middle East and North America. A strain of WNV, Kunjin (WNV_KUN), is endemic in northern Australia and infection with this virus is generally asymptomatic. However in early 2011, an unprecedented outbreak of encephalitis in horses occurred in south-eastern Australia, resulting in mortality in approximately 10%–15% of infected horses. A WNV-like virus (WNV_NSW2011) was isolated and found to be most closely related to the indigenous WNV_KUN, rather than other exotic WNV strains. Furthermore, at least two amino acid changes associated with increased virulence of the North American New York 99 strain (WNV_NY99) compared to the prototype WNV_KUN were present in the WNV_NSW2011 sequence. This review summarizes our current understanding of WNV_KUN and how the epidemiology and ecology of this virus has changed. Analysis of virulence determinants of contemporary WNV_KUN isolates will provide clues on where virulent strains have emerged in Australia. A better understanding of the changing ecology and epidemiology associated with the emergence of virulent strains is essential to prepare for future outbreaks of WNV disease in Australia.

Clinical and radiological predictors of outcome for Murray Valley encephalitis

A review of the laboratory-confirmed cases of Murray Valley encephalitis (MVE) from Western Australia between 2009 and 2011 was conducted to describe the clinical, laboratory, and radiological features of the disease. The nine encephalitis patients presented with altered mental state and seizures, tremor, weakness, or paralysis. All patients developed a raised C-reactive protein, whereas most developed acute liver injury, neutrophilia, and thrombocytosis. All patients with encephalitis developed cerebral peduncle involvement on early magnetic resonance imaging (MRI). The absence of thalamic MRI hyperintensity during the acute illness, with or without leptomeningeal enhancement, predicted a better neurological outcome, whereas those patients with widespread abnormalities involving the thalamus, midbrain, and cerebral cortex or the cerebellum had devastating neurological outcomes. MRI scans repeated months after acute illness showed destruction of the thalamus and basal ganglia, cortex, or cerebellum. These findings may help clinicians predict the neurological outcome when evaluating patients with MVE.

Seasonality of viral infections: mechanisms and unknowns

Seasonality is a long-recognized attribute of many viral infections of humans, but the mechanisms underlying seasonality, particularly for person-to-person communicable diseases, remain poorly understood. Better understanding of drivers of seasonality could provide insights into the relationship between the physical environment and infection risk, which is particularly important in the context of global ecological change in general, and climate change in particular. In broad terms, seasonality represents oscillation in pathogens' effective reproductive number, which, in turn, must reflect oscillatory changes in infectiousness, contact patterns, pathogen survival, or host susceptibility. Epidemiological challenges to correct identification of seasonal drivers of risk include failure to adjust for predictable correlation between disease incidence and seasonal exposures, and unmeasured confounding. The existing evidence suggests that the seasonality of some enteric and respiratory viral pathogens may be driven by enhanced wintertime survival of pathogens, and also by increased host susceptibility resulting from relative 'wintertime immune suppression'. For vector-borne diseases and zoonoses, environmental influences on vector or reservoir abundance, and vector biting rates, are probably more important. However, numerous areas of uncertainty exist, making this an exciting area for future research.