Lassa fever is caused by a viral haemorrhagic arenavirus that affects two to three million people in West Africa, causing a mortality of between 5,000 and 10,000 each year. The natural reservoir of Lassa virus is the multi-mammate rat Mastomys natalensis, which lives in houses and surrounding fields. With the aim of gaining more information to control this disease, we here carry out a spatial analysis of Lassa fever data from human cases and infected rodent hosts covering the period 1965–2007. Information on contemporary environmental conditions (temperature, rainfall, vegetation) was derived from NASA Terra MODIS satellite sensor data and other sources and for elevation from the GTOPO30 surface for the region from Senegal to the Congo. All multi-temporal data were analysed using temporal Fourier techniques to generate images of means, amplitudes and phases which were used as the predictor variables in the models. In addition, meteorological rainfall data collected between 1951 and 1989 were used to generate a synoptic rainfall surface for the same region. Three different analyses (models) are presented, one superimposing Lassa fever outbreaks on the mean rainfall surface (Model 1) and the other two using non-linear discriminant analytical techniques. Model 2 selected variables in a step-wise inclusive fashion, and Model 3 used an information-theoretic approach in which many different random combinations of 10 variables were fitted to the Lassa fever data. Three combinations of absence∶presence clusters were used in each of Models 2 and 3, the 2 absence∶1 presence cluster combination giving what appeared to be the best result. Model 1 showed that the recorded outbreaks of Lassa fever in human populations occurred in zones receiving between 1,500 and 3,000 mm rainfall annually. Rainfall, and to a much lesser extent temperature variables, were most strongly selected in both Models 2 and 3, and neither vegetation nor altitude seemed particularly important. Both Models 2 and 3 produced mean kappa values in excess of 0. 91 (Model 2) or 0. 86 (Model 3), making them ‘Excellent’. The Lassa fever areas predicted by the models cover approximately 80% of each of Sierra Leone and Liberia, 50% of Guinea, 40% of Nigeria, 30% of each of Côte d' Ivoire, Togo and Benin, and 10% of Ghana.
Lassa fever (LF) is a viral haemorrhagic fever the pathogenic agent of which is an arenavirus Lassa virus (LASV) first discovered in 1969 in Nigeria, in a missionary nurse living in Lassa, a village close to the border with Cameroon [1]. Lassa fever is widespread in West Africa, affecting 2 million persons per annum with 5,000–10,000 fatalities annually [2]. Since its initial discovery, nosocomial outbreaks of Lassa fever have occurred repeatedly in Sierra Leone: Panguma, Kenema, 1971–83,1997, Liberia: Zorzor, 1972; Phebe 1972,1977,1982; Ganta 1977,1982 and Nigeria: Jos, 1970,1993; Onitsha, 1974; Zonkwa, 1975; Vom, 1975–77, Imo, 1989; Lafia, 1993; and Irrua, 2004 [3], [4], [5], [6], [7], [8], [9]. In Guinea, some acute but isolated cases were recorded in hospitals [10] and a single rural outbreak was recorded on the Sierra Leone border in 1982–83 [11]. Between these two areas, namely in Côte d' Ivoire, Ghana, Togo and Benin, no outbreak has ever been recorded, though isolated cases show evidence of viral circulation in that area [12], [13], [14]. Lassa fever therefore appears to have 2 geographically separate endemic areas: the Mano River region (Guinea, Sierra Leone, Liberia) in the West, and Nigeria in the East. The reservoir host of this virus is the multimammate rat, Mastomys natalensis, which was found infected for the first time in Sierra Leone and in Nigeria in 1972 [15], [16], and recently in Guinea [17]. In Upper Guinea, these commensal rodents aggregate in houses during the dry season, and disperse into the surrounding fields in the rainy season, foraging in cultivated areas before harvesting [18]. Villages where LASV-positive rodents have been trapped are all located in rain forest areas or in the transition zone between forest and savannah, within the 1500 mm rainfall isohyet. Rainfall seems to be an important ecological factor because a recent longitudinal study in rodents demonstrated that LASV infection was two to three times higher in the rainy season than in the dry season [18]. There are no studies to date indicating that the virus can survive better in humid than in dry soil, but evidence points in this direction. For example, the recent discovery of a new arenavirus in Mus minutoides (Kodoko virus [19]) and of hantavirus in Hylomyscus simus (Sangassou virus) in Guinea [20], were both made in rodents trapped in wet habitats, swamps or along river edges. In the USA, many new hantaviruses discovered within the last 15 years are found in damp or wet places such as arroyos or canyons, i. e. Black Creek canal virus, Blue river virus, El moro Canyon virus, Limestone Canyon virus. In the case of Sin Nombre virus, responsible for hemorrhagic fever with pulmonary syndrome, high risk areas are associated with higher elevation and mesic vegetation whereas low risk areas are associated with lower elevation and xeric vegetation. Soil moisture appears to be a key factor explaining the maintenance of this virus in high risk areas [21], [22]. In Europe, the transmission and persistence of Puumala virus, responsible for nephropathia epidemica, seems possible only if indirect transmission through a contaminated environment is included in a mathematical model. The combination of viral dynamics inside and outside the host, rodent demographic patterns and humid periods seems to explain the geographical distribution of this disease [23]. These advances all indicate the possible importance of rainfall patterns and humidity for Lassa Fever. We present our analysis of LF in West Africa in three steps: a first univariate analysis linking LF with high rainfall areas (Model 1) and the other two, multivariate analyses quantifying associations between LASV presence and a number of environmental parameters, derived from earth-observing satellites, that lead to the production of the first predictive risk maps for Lassa fever. One of these multivariate modelling approaches uses step-wise variable selection procedures (Model 2) whilst the other uses random combinations of predictor variables to identify the individual best predictors of LASV presence and absence (Model 3).
Figure 1 shows the location of LF outbreaks (or areas of high human seroprevalence) from 1951 to 1989. The Jos plateau in Nigeria receives more rainfall than the surrounding areas and is disconnected from the wet coastal area by lowland areas of lower rainfall. Only the initial case in Lassa (800 mm/year) is located outside the high rainfall area. The map in Figure 1 suggests that areas with between 1200 mm and 1500 mm of rainfall per year are at relatively low risk of LF; areas with above 1500 mm have a much higher risk and, finally, areas with in excess of 3000 mm of rainfall annually appear to be at zero risk (i. e. had no outbreaks of LF in that period), although these very high rainfall areas are not widespread. The predictor variables chosen for the three different cluster versions of Model 2 are shown in Table 2 with their mean ranks across the 100 bootstrap models for each. The average accuracy of these models is shown in Table 3 and the mean values of the selected predictor variables for one of the top models from the 2 Absence: 1 Presence cluster combination is shown in Table 4. Figure 2 shows the mean predicted risk map of LF from the 100 bootstrap models using this same combination of absence and presence clusters. With only one cluster each, LF appeared to be over-predicted whilst with two clusters each LF appeared to be more strongly limited to the training set data points and their immediate surrounding areas (i. e. the disease was possibly under-predicted). The 2 Absence∶1 Presence cluster combination was therefore considered to give the best overall result. The rainfall variables were disproportionately selected by all cluster combinations in Model 1; each ‘top ten’ list in Table 2 contains four such variables, where the random expectation (5 satellite channels) is only two. At the same time, the vegetation index channels (NDVI and EVI) are under-represented, with only a single one of 20 such variables (10 Fourier variables per channel) chosen across all cluster combinations; the balance of the important predictor variables were thermal ones (either LST or MIR). The relatively high values for the average ranks of even the top variables in all cluster combinations in Table 2, however, reflects the fact that each of the 100 bootstrap samples gave rather different results in terms of the variables selected, and in their order of selection. This is a common feature of relatively small datasets. Despite the variability in the selected predictor variables, mean model accuracies were very high (Table 3) with, as expected, model accuracy increasing with increasing cluster numbers. The mean values of kappa put all models well within the ‘Excellent’ category of the Landis and Koch [41] scale (where kappa<0. 4 is ‘Poor’; 0. 4<kappa<0. 75 is ‘Good’; and kappa>0. 75 is ‘Excellent’). The mean values of the key predictor variables may differ considerably, or only by rather small amounts (Table 4). Table 4 shows that the mean values for the single clusters of presence points in the model are often intermediate between those of the two absence clusters. This applies to mean rainfall, night-time LST minimum, MIR phase 2 and daytime LST (mean and maximum). In other cases, mean values for the presence points are well outside those for either absence cluster. This applies to rainfall (amp1, amp3, phase1 and minimum) and NDVI phase 3. Concentrating on the important rainfall variables in Table 4 it is possible to suggest that LASV requires high (but not the highest) mean rainfall areas (rain mean), but with very high annual variation of this variable (rain amp1), and with peak rainfall occurring much later in the year (during August rather than during May or March, the months of peak rainfall of the absence clusters in Table 4, rain phase1). The significance of the higher amp3 rainfall value in Table 2 (the first selected variable) is unclear; often such higher harmonics act to modulate the lower frequency – annual or bi-annual – harmonics, and thus adjust the seasonal pattern of rainfall (extending or reducing high rainfall periods, depending on the timing of this tri-annual harmonic). The predicted risk map (Figure 2) captures most of the presence points in the database (the grey areas in Figure 2 in southern Nigeria and Cameroon are regions where cloud contamination is so continuous that it was not possible to obtain either sufficient cloud-free images or their temporal Fourier derivatives for modelling; these are therefore areas where it is not possible to make predictions of risk). The predicted risk areas in Figure 2 contract towards the coast in the ‘Dahomey gap’ between the western and central forests of Africa (see Introduction) but are still more extensive than the rainfall map and data in Figure 1 suggest. In fact the satellite rainfall image (CMORPH mean, not shown) also indicates a lower mean rainfall area in this region, so that the positive LASV predictions for this area must arise from the values of other key predictor variables. The differences between Figure 1 and Figure 2 in the basin of the River Zaire, towards Central Africa, arise because these areas (though high in rainfall) are environmentally quite distinct from those of the training set area and so the risk map models classify them as ‘No prediction’ areas (coloured grey in Figure 2). Tables 4 and 5 show results analogous to those of Tables 2 and 3 but for Model 3, where the important variables were identified using the combination method of Burnham and Anderson [40]. This method highlights even more the importance of rainfall variables (only 8 out of the 30 variables in Table 5 are not directly rainfall related), with slightly different combinations in each case for the different cluster combinations. Overall model accuracies are still excellent (Table 6) though not quite as good as those for Model 2. Figure 3 shows the mean predicted risk map obtained by using in Model 3 the selected combination of the top 10 variables for the same 100 bootstrap samples that were used in Model 2 to generate Figure 2. Figure 3 is less equivocal about risk areas than is Figure 2 (i. e. there are fewer regions of intermediate probability of LASV risk) for the simple reason that the same 10 variables were used throughout, whereas different combinations of variables were often selected in the Model 2 models, giving more variable results. Figure 3 again captures most of the presence sites within the training set, with rather different predictions for the Dahomey Gap region than those in Figure 2.
The question that comes immediately to mind is: why does Lassa fever occur only in West Africa, whereas the range of its vertebrate host extends into East and Southern Africa? This is a recurrent question for other rodent-borne diseases (such as plague and hemorrhagic fevers with renal or pulmonary syndrome; see [42] for a review), which are also much more restricted in their distributions than are their hosts. Our analyses here show quite clearly that Lassa fever requires a particular combination of high (but not the highest) rainfall, and with a particular form of variability and seasonal timing, whereas its hosts can and do occur over regions experiencing a much wider range of rainfall conditions. Temperature appears to be less important in determining LASV distribution, although there are large differences between different areas; for example the annual mean and maxima in high risk areas are 27°C and 32°C respectively, whereas in low risk areas the mean temperature was approx. 38°C. Such high temperatures are known to increase LASV decay [43]. One curious feature of the present results is the seeming unimportance of vegetation variables in the predictor data sets. This lack of importance is not due to their strong correlation with rainfall variables (such a correlation might exclude them in step-wise inclusive variable selection), because Model 3 (using a method that avoids the problems of step-wise methods) independently and quite categorically failed to identify vegetation variables as important in determining LASV distribution. Taken together these results suggest that the survival of the virus outside of the vertebrate host might be a key to determining its distribution, and that this survival depends upon moisture or rainfall conditions above more or less all other environmental variables. This result differs from the conditions favouring other viral transmission; for example, low relative humidity and temperature favour avian influenza [44]. In the case of Lassa, the virus appears to survive better in humid conditions, during the rainy season. Rodents will be more often contaminated during their frequent movements at this season, for mating or dispersing into the surrounding fields [45]. Conversely, viral aerosol stability, seems to be higher when the humidity is lower [43], a condition that obviously occurs more frequently in the dry season. The experiments of Stephenson help to explain the numerous LF cases recorded in hospitals during the late dry season, between January and March in Sierra Leone and Nigeria ([46], Omilabu, pers. com.) but they do not necessarily throw much or any light on the persistence of Lassa fever in the general environment. We suggest that rainfall, within defined limits, is the single most important abiotic determinant of this persistence. M. natalensis, the most important host of LASV, does not occur in the western part of the region, in coastal Guinea and Sierra Leone and west to the 12th meridian. Only M. erythroleucus occurs in these regions, and our surveys have always found it to be negative for LASV infections [17]. The low human sero-prevalences recorded in these coastal areas are most likely due to the movement of people from highly endemic zones, or to human-to-human transmission. Towns and villages in these coastal areas, from Guinea to Gabon, have been invaded by the black rat Rattus rattus, and the domestic mouse, Mus musculus, probably taken there in historical times by Arab and European traders, explorers and colonisers. Absence of M. natalensis from coastal areas, for whatever reason (e. g. unsuitable habitats, or competition from other, non-Lassa-reservoir rodents), would explain the absence of Lassa fever in these areas, despite the apparently favourable (for LASV) climatic conditions (although the models suggest that some areas may be too wet for LASV). In Conakry for example, rodent sampling (330 specimens) showed that the most abundant species was M. musculus (70%), followed by R. rattus (25%) (unpublished data). In East and South Africa, the same reservoir species is present but the virus is replaced by other Lassa-like viruses such as Ippy, Morogoro and Mopeia, found in M. natalensis in CAR, Tanzania, Mozambique and Zimbabwe (CRORA database in Pasteur Institute website, http: //www. pasteur. fr/recherche/banques/CRORA/, [47], [48], [49]). These different Lassa –like viruses are not known to be pathogenic in humans and are considered ancestral by phylogenetic studies [50]. The scenario of multiple infection with both Lassa-like and Lassa virus is highly unlikely, and so we consider that central and eastern Africa are Lassa free. This is supported by many negative serological studies in Cameroon, in CAR, Congo, Equatorial Guinea and Gabon [24], [25], [26], [27]. However, the situation in south-west Cameroon bordering Nigeria remains problematic because this zone appears to be at high risk according to Figure 2. This is a volcanic area, which could provide a geographic barrier (Mt Cameroon, 4100 m, and the volcano chain up to the Adamaoua plateau). Furthermore, another species of Mastomys is suspected to be present in this area, M. kollmannspergeri, which is found in Niger, NE Nigeria, N Cameroon, S. Sudan and Chad [51]. In Zakouma National Park in Chad, some specimens were found in a village and in camps, indicating a potential synanthropy of this species [52]. The predictive risk map in Figure 2 identifies the central parts of Cameroon and CAR as risky areas, where it is possible that other Lassa-like viruses could occur, intermediate between Ippy/Mobala and Lassa (Mobala is another Lassa-like virus found in Praomys sp. , a closely related species to Mastomys spp, in CAR [53].). According to the risk maps shown here, with the reservations noted above, the LF risk area covers approximately 80% of the area of each of Sierra Leone and Liberia, 50% of Guinea, 40% of Nigeria, 30% of each of Côte d' Ivoire, Togo and Benin and 10% of Ghana. Such maps help public health policies and research, in targeting disease control and studies in potentially infected areas.
Previous studies on the eco-epidemiology of Lassa fever in Guinea, West Africa, have shown that the reservoir is two to three times more infected by Lassa virus in the rainy season than in the dry season. None of the intrinsic variables of the murine population, such as abundance or reproduction, was able to explain this seasonal variation in prevalence. We therefore here investigate the importance of extrinsic environmental variables, partly influenced by the idea that in the case of nephropathia epidemica in Europe contamination of the environment, and therefore survival of the pathogen outside the host, appears to be an important factor in this disease' s epidemiology. We therefore made an extensive review of the literature, gathering information about the geographical location of sites where Lassa fever has been certainly identified. Environmental data for these sites (rainfall, temperature, vegetation and altitude) were gathered from a variety of sources, both satellites and ground-based meteorological stations. Several statistical treatments were applied to produce Lassa ‘risk maps’. These maps all indicate a strong influence of rainfall, and a lesser influence of temperature in defining high risk areas. The area of greatest risk is located between Guinea and Cameroon.
infectious diseases/neglected tropical diseases
computational biology/ecosystem modeling
infectious diseases/viral infections
virology/emerging viral diseases
Risk Maps of Lassa Fever in West Africa