Modeling the dynamics of Lassa fever in Nigeria

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Abstract

Lassa fever is a zoonotic disease spread by infected rodents known as multimammate rats. The disease has posed a significant and major health challenge in West African countries, including Nigeria. To have a deeper understanding of Lassa fever epidemiology in Nigeria, we present a deterministic dynamical model to study its dynamical transmission behavior in the population. To mimic the disease’s biological history, we divide the population into two groups: humans and rodents. We established the quantity known as reproduction number \[{\mathcal {R}}_{0}\] . The results show that if \[{\mathcal {R}}_{0} <1\] then the system is stable, otherwise it is unstable. The model fitting was performed using the nonlinear least square method on cumulative reported cases from Nigeria between 2018 and 2020 to obtain the best fit that describes the dynamics of this disease in Nigeria. In addition, sensitivity analysis was performed, and the numerical solution of the system was derived using an iterative scheme, the fifth-order Runge–Kutta method. Using different numeric values for each parameter, we investigate the effect of all highest sensitivity indices’ parameters on the population of infected humans and infected rodents. Our findings indicate that any control strategies and methods that reduce rodent populations and the risk of transmission from rodents to humans and rodents would aid in the population’s control of Lassa fever.

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On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations.

O. Diekmann, J. A. P. (Hans) Heesterbeek, J. A. Metz (1990)

The expected number of secondary cases produced by a typical infected individual during its entire period of infectiousness in a completely susceptible population is mathematically defined as the dominant eigenvalue of a positive linear operator. It is shown that in certain special cases one can easily compute or estimate this eigenvalue. Several examples involving various structuring variables like age, sexual disposition and activity are presented.

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Risk Maps of Lassa Fever in West Africa

Elisabeth Fichet-Calvet, David Rogers (2009)

Introduction 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). Materials and Methods Model 1 Disease Data Nosocomial outbreaks and prevalences of Lassa fever in humans were derived from the dataset, and were placed on a map of West and Central Africa (see table 1 for the detailed references by country). The null prevalences recorded in Cameroon, CAR, Gabon and Congo were derived from samples taken in towns [24],[25],[26], whereas the low prevalence of 5% recorded in Pool region in Congo came from samples taken in villages [27]. Elsewhere, prevalences appear as a mean, estimated regionally from several villages or from hospital staffs. Data on human infections cover the period 1965 to 2007. 10.1371/journal.pntd.0000388.t001 Table 1 Positive localities recorded from humans and rodents indicating the presence of Lassa virus in West Africa. Country Administrative region Town/village/hospital Latitude Longitude Year Reference for humans Reference for rodents Benin Borgou department Bambéréké hosp. 10.23 2.66 1977 [14] Burkina Comoé province Banfora 10.63 −4.77 1974 [54] Congo Pool region Ngamambou −4.33 14.85 1981 [27] Cote d'Ivoire Beoumi prefecture Beoumi 7.67 −5.57 1970–74 [54] Cote d'Ivoire Duekoue prefecture Forêt Classée 6.66 −7.07 2000 [12] Cote d'Ivoire Guiglo prefecture Guiglo 6.54 −7.48 2000 [12] Guinea Faranah prefecture Bantou 10.07 −10.58 2003–05 [17],[18] Guinea Faranah prefecture Gbetaya 9.84 −11.03 1990–92, 1996–97, 2003–05 [55] [17],[18],[56] Guinea Faranah prefecture Kamaraya 9.88 −10.75 1990–92 [55] Guinea Faranah prefecture Sangoyah 9.72 −10.88 1990–92, 1996–97 [55] [56] Guinea Faranah prefecture Tanganya 10.00 −10.97 2003–05 [17],[18] Guinea Faranah prefecture Tindo 9.97 −10.70 1990–92 [55] Guinea Gueckedou prefecture Bawa 8.56 −10.03 1990–92, 1996–97 [55] [56] Guinea Gueckedou prefecture Denguedou 8.49 −10.44 1993, 2005 [57] [17] Guinea Gueckedou prefecture Fangamandou 8.50 −10.60 1990–92, 1993, 1996–97 [55],[57] [56] Guinea Gueckedou prefecture Guedembou 8.76 −9.99 1993 [57] Guinea Gueckedou prefecture Kassadou 8.91 −10.35 1993 [57] Guinea Gueckedou prefecture Kpolodou 8.85 −10.34 1993 [57] Guinea Gueckedou prefecture Nongoa Mbalia 8.70 −10.37 1990–92 [55] Guinea Gueckedou prefecture Owe Jiba 8.48 −10.44 1990–92, 1996–97 [55] [56] Guinea Gueckedou prefecture Sassani Toli 8.75 −10.30 1990–92 [55] Guinea Gueckedou prefecture Tekoulo 8.54 −10.01 1993, 1996–97 [57] [56] Guinea Gueckedou prefecture Telekolo 8.47 −10.43 1990–92 [55] Guinea Gueckedou prefecture Temessadou 8.66 −10.31 1993 [57] Guinea Gueckedou prefecture Tomandou 8.50 −10.30 1993 [57] Guinea Kindia prefecture Madina Oula 9.88 −12.45 1982–83, 1990–92, 1996–97 [11],[55] [56] Guinea Kissidougou prefecture Bambaya 9.30 −10.10 1996–99 [10] Guinea Kissidougou prefecture Banankoro 9.18 −9.30 1996–99 [10] Guinea Kissidougou prefecture Boue 9.01 −9.95 1996–97 [56] Guinea Kissidougou prefecture Fedou 9.20 −9.90 1996–99 [10] Guinea Kissidougou prefecture Telekoro 9.18 −10.10 1965, 1967, 1968, 1996–99 [10],[14] Guinea Kissidougou prefecture Yende Milimou 8.89 −10.17 1996–99 [10] Guinea Lola prefecture Gbah 7.62 −8.55 1990–92 [55] Guinea Lola prefecture Gbenemou 7.71 −8.52 1990–92 [55] Guinea Lola prefecture Thuo 7.58 −8.50 1990–92 [55] Guinea Macenta prefecture Lorlu 8.56 −10.02 1996–97 [56] Guinea Nzérékoré prefecture Bignamou 7.33 −9.10 1996–99 [10] Guinea Nzérékoré prefecture Dieke 7.35 −8.95 1996–99 [10] Guinea Nzérékoré prefecture Koulenin 7.75 −8.82 1996–99 [10] Guinea Siguiri prefecture Balato 11.57 −9.32 1990–92 [55] Guinea Yomou prefecture Bamakama 7.72 −9.27 1990–92, 1996–97 [55] [56] Guinea Yomou prefecture Komore 7.66 −9.26 1990–92 [55] Guinea Yomou prefecture Waita 7.56 −9.26 1990–92, 1996–97 [55] [56] Liberia Bomi county Goodrich plantation hosp. ( = Klay) 6.69 −10.87 1980 [58] Liberia Bong county Suakoko (Phebe hosp.) 7.19 −9.38 1972 [5],[59],[60] Liberia Grand Cape Mont county Mano river hosp. ( = Kongo) 7.33 −11.14 1980 [58] Liberia Lofa county Foya Kamara hosp. 8.36 −10.21 1977, 1979, 1980, 1981 [58],[60],[61] Liberia Lofa county Koindu 8.22 −10.77 1974 [59] Liberia Lofa county Yielah 7.82 −9.402 1972 [14] Liberia Lofa county Zigida 8.04 −9.49 1972 [62],[63] Liberia Lofa county Zorzor hosp. 7.78 −9.43 1969, 1972,1977, 1979, 1980–82 [5],[58],[59],[60] Liberia Nimba county Ganta hosp. 7.23 −8.98 1982, 2004 [5],[58] Liberia Nimba county Louplay 6.95 −8.71 2006 [64] Liberia Nimba county Saglelpie 6.96 −8.84 2007 [65] Mali Segou region Ntorosso 13.9 5.4 1971 [54] Nigeria Adamawa state Takum 7.27 9.98 1974 [54] Nigeria Anambra state Onitsha hosp. 6.17 6.78 1974 [4] Nigeria Benue state Gboko 7.32 9.00 1987 [66] Nigeria Borno state Lassa 10.68 13.27 1969 [1] Nigeria Edo state Ekpoma 6.75 6.13 2001–04 [9] Nigeria Edo state Ibilo 7.43 6.08 2001–04 [9] Nigeria Edo state Igarra 7.28 6.10 2001–04 [9] Nigeria Imo state Aba hosp. 5.12 7.37 1989 [6] Nigeria Imo state Aboh Mbaise hosp. 5.55 7.20 1989 [6] Nigeria Kaduna state Rahama 10.42 8.68 1952 [67] Nigeria Nasarawa state Lafia hosp. 8.48 8.52 1987, 1992–93 [7],[66] Nigeria Ondo state Ondo 7.10 4.83 1987 [66] Nigeria Plateau state Bassa 9.93 8.73 1970 [3] Nigeria Plateau state Fan 8.82 10.90 1977 [14] Nigeria Plateau state Jos 9.92 8.90 1970, 1972, 1973, 1992–93 [7],[54] Nigeria Plateau state Ner-Pankshin 9.33 9.45 1972 [16] Nigeria Plateau state Vom 9.73 8.78 1974–75, 1976, 1977 [14] Nigeria Plateau state Zonkwa 9.78 8.28 1975 [14] Nigeria Sokoto state Sokoto 13.06 5.25 1971 [54] Nigeria Taraba state Gongola 8.50 11.50 1987 [66] Nigeria Taraba state Jalingo 8.88 11.36 2007 [68] Sierra Leone Bo district Bo hosp. 7.96 −11.74 2001 [69] Sierra Leone Bo district Gerihun camp 7.93 −11.58 2003 [70] Sierra Leone Bo district Jimmi camp 7.60 −11.82 2003 [70] Sierra Leone Bombali district (North) Kamabunyele 9.18 −11.93 1977–1983 [71] Sierra Leone Bombali district (North) Kathumpe 9.50 −12.23 1977–1983 [71] [71] Sierra Leone Bombali district (North) Mamaka 9.10 −12.32 1977–1982 [71] [71] Sierra Leone Kailahun district (East) Daru hosp. 7.99 −10.85 2000 [72] Sierra Leone Kailahun district (East) Kailahun hosp. 8.28 −10.57 2001 [69] Sierra Leone Kenema district (East) Bomie/Landoma 8.23 −11.07 1977–83, 1996–97 [71],[73] Sierra Leone Kenema district (East) Buima 8.27 −11.11 1996–97 [73] Sierra Leone Kenema district (East) Daabu 7.92 −10.95 1996–97 [73] Sierra Leone Kenema district (East) Giema 8.20 −11.05 1977–82 [71] Sierra Leone Kenema district (East) Kenema hosp. 7.90 −11.20 1996–97, 1999, 2001–04 [69],[70],[73],[74] Sierra Leone Kenema district (East) Konia 8.10 −11.02 1977–83 [71] [71] Sierra Leone Kenema district (East) Kpandebu 8.22 −11.07 1977–83 [71] [71] Sierra Leone Kenema district (East) Lalehun 8.20 −11.08 1977–82 [71] Sierra Leone Kenema district (East) Largo camp 8.05 −11.12 2003 [70] Sierra Leone Kenema district (East) Lowoma 8.22 −11.03 1977–82 [71] [71] Sierra Leone Kenema district (East) Macca 8.15 −11.22 1996–97 [73] Sierra Leone Kenema district (East) Neama 8.12 −11.00 1977–83 [71] Sierra Leone Kenema district (East) Niahun 8.00 −11.07 1977–83 [71] [71] Sierra Leone Kenema district (East) Njakundoma 8.23 −11.05 1977–83 [71] [71] Sierra Leone Kenema district (East) Nongowa 7.63 −11.40 2003 [70] Sierra Leone Kenema district (East) Palima/Tongola 8.22 −11.05 1977–83, 1996–97 [71],[73] [71] Sierra Leone Kenema district (East) Pandebu 8.21 −11.13 1996–97 [73] Sierra Leone Kenema district (East) Panguma hosp. 8.20 −11.22 1970–75, 1996–97, 2003 [70],[73],[75],[76],[77] [15] Sierra Leone Kenema district (East) Segbwema hosp. 8.00 −10.95 1975, 1977–83, 1996–97 [73],[77] [71] Sierra Leone Kenema district (East) Semewabu 8.02 −10.87 1977–83 [71] Sierra Leone Kenema district (East) Serabu hosp. 7.85 −11.29 1977 [14] Sierra Leone Kenema district (East) Tokpombu 8.22 −11.09 1996–97 [73] Sierra Leone Kenema district (East) Tongo field 8.45 −11.12 1972 [15] Sierra Leone Kenema district (East) Tongo hosp. 8.45 −11.28 1970–72, 1996–97 [73],[75] Sierra Leone Kono district Kono hosp. 8.75 −11.00 2001 [69] Sierra Leone Moyamba district Taiama camp 8.20 −12.07 2003 [70] Sierra Leone Pujehun district Pujehun hosp. 7.35 −11.72 2001 [69] Year indicates the time of collection. Climatic data A synoptic rainfall map of West Africa was obtained from L'Hôte&Mahé [28] and is shown in Figure 1. This synoptic map is derived from rainfall records for the period 1951 to 1989. In West Africa, the highest rainfall regions are located either side of the Dahomey gap, which separates the 2 great rainforest zones of Guinea and Congo, each region receiving more than 1500 mm of rainfall per year. On the western side, the region includes Guinea, Sierra Leone, Liberia, the extreme West of Côte d'Ivoire and coastal Ghana. The eastern side includes the Congolese zone and south eastern Nigeria (Figure 1). 10.1371/journal.pntd.0000388.g001 Figure 1 West and Central Africa mean annual rainfall (1951–1989 [28]), Lassa fever nosocomial outbreaks (stars) and human seroprevalence (numbers in %). Models 2 and 3 Disease data The new Lassa fever database was developed with all indications of Lassa fever presence in West Africa in the period 1965 to 2007. These indications included sero- and virologically positive rodents and human beings. For the rodents, all the localities where M. natalensis was screened for LASV were included. Localities were defined as positive when at least one M. natalensis was positive, and negative when none was infected. Because of the heterogeneous data for humans, the database was more complicated to establish. The localities were defined as positive when clinical cases were confirmed by a laboratory test or when sampled populations had a seroprevalence ≥10%. The ‘negative’ localities were defined when seroprevalence was 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). Model 3 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. 10.1371/journal.pntd.0000388.g003 Figure 3 Mean predicted Lassa risk map for West Africa from the Model 3 series with two absence and one presence clusters, with positive localities indicated by stars. Other information as for Figure 2. 10.1371/journal.pntd.0000388.t005 Table 5 Mean ranking of the key predictor variables across 100 bootstrap models for each Absence∶Presence cluster combination for Model 3. Absence∶Presence clusters 1∶1 2∶1 2∶2 Variable Mean rank Variable Mean rank Variable Mean rank 1 Rain phase1 1 Rain amp1 1 EVI variance 1 2 Rain phase2 2 Rain mean 2 Rain variance 2 3 Rain amp1 3 Rain phase1 3 Rain max. 3 4 Rain amp3 4 EVI phase3 4 Rain phase2 4 5 Rain mean 5 Rain max. 5 Rain amp3 5 6 Rain max. 6 Rain phase2 6 NDVI variance 6 7 Rain variance 7 Rain amp3 7 Rain amp1 7 8 Rain amp2 8 dLST phase1 8 dLST mean 8 9 Rain min. 9 Rain min. 9 dLST amp2 9 10 EVI variance 10 EVI variance 10 Rain min. 10 10.1371/journal.pntd.0000388.t006 Table 6 Mean accuracy statistics across 100 bootstrap models for each Absence∶Presence cluster combination for Model 3 (see text for definitions). Accuracy Absence∶Presence clusters 1∶1 2∶1 2∶2 Kappa 0.86 0.867 0.917 Sensitivity 93% 94% 96% Specificity 93% 92% 95% AUC 0.975 0.98 0.988 AICc 162.8 132 80.4 Discussion 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. Supporting Information Alternative Language Abstract S1 Translation of the abstract into French by Elisabeth Fichet-Calvet (0.02 MB DOC) Click here for additional data file.

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Evaluation of rodent control to fight Lassa fever based on field data and mathematical modelling

Joachim Mariën, Benny Borremans, Fodé Kourouma … (2019)

ABSTRACT The Natal multimammate mouse (Mastomys natalensis) is the reservoir host of Lassa virus, an arenavirus that causes Lassa haemorrhagic fever in humans in West Africa. Because no vaccine exists and therapeutic options are limited, preventing infection through rodent control and human behavioural measures is currently considered to be the only option. In order to assess the efficacy of rodent control, we performed a 4-year field experiment in rural Upper Guinea and developed a mathematical model to simulate different control strategies (annual density control, continuous density control, and rodent vaccination). For the field study, rodenticide baits were placed each year in three rural villages, while three other villages were used as controls. Rodents were trapped before and after every treatment and their antibody status and age were determined. Data from the field study were used to parameterize the mathematical model. In the field study, we found a significant negative effect of rodent control on seroprevalence, but this effect was small especially given the effort. Furthermore, the rodent populations recovered rapidly after rodenticide application, leading us to conclude that an annual control strategy is unlikely to significantly reduce Lassa virus spillover to humans. In agreement with this finding, the mathematical model suggests that the use of continuous control or rodent vaccination is the only strategy that could lead to Lassa virus elimination. These field and model results can serve as a guide for determining how long and frequent rodent control should be done in order to eliminate Lassa virus in rural villages.