Scrub typhus, an acute febrile zoonotic disease originating from Japan, is caused by bacteria called orientia tsutsugamushi 1899 [1
]. It is transmitted through the bite of infected mites (the larval stage; known as chiggers) [2
] and is prevalent in many countries in Asia-Pacific regions, extending from northern Japan and far-eastern Russia in the north, to northern Australia in the south, and Pakistan in the west [3
]. Almost a million scrub typhus cases are reported annually, and a billion people living in this region are under the risk of the disease [4
]. One recent report [5
] claims that scrub typhus has been reported from well beyond the limits of the Tsutsugamushi Triangle and that has triggered concerns about the worldwide presence of scrub typhus [5
]. In Nepal, scrub typhus has been a major public health problem with a series of outbreak across the country following the devastating earthquake of April 2015, with rising cases and spatial spread in the subsequent years [7
Previous studies have reported the association of scrub typhus with climate, topography, vegetation dynamics, and socioeconomic factors. Suitable environmental conditions can provide ideal habitats for vectors to breed, become bacterium, survive long enough to become infectious, and finally transmit the disease to a susceptible human host [8
]. For example, the spatial distribution of scrub typhus was associated with land use, NDVI, and relative humidity in southern India [6
]. Positive association with temperature, relative humidity, and rainfall was also observed in southern China [9
]. In Taiwan, the spatial pattern of the scrub typhus was positively associated with cropland, vegetation mosaic, and elevation [11
]. Rainfall provides the moisture necessary for the survival and growth of host rodents and shows a positive association with rodent density [10
]. The occurrence of scrub typhus is also linked to anthropogenic activities and socioeconomic factors. Farmworker population density and timber management are also positively associated with scrub typhus [12
]. Changes in land use, animal populations, and climate, primarily due to increasing human populations, drive the emergence of zoonosis [13
]. Elevated risk was also observed proximate to cultivated land [14
]. A recent study in South Korea revealed a positive association between deforestation and scrub typhus [15
Mapping the environmental suitability for disease transmission and identifying the associated environmental variables can provide crucial information for evidence-based decision making [16
]. A better understanding of the spatial distribution of the incidence along with the possible associated factors of the distribution allows for more targeted disease control efforts and assist in the prediction of disease dynamics [16
The ecological niche modeling (ENM) approach, machine learning algorithms, and GIS and remote sensing technology are playing an important role in disease mapping, identifying the associated environmental factors and advancing geography of infectious diseases [16
]. There are several advantages of ENM predictive modeling in estimating disease distribution patterns over the traditional data-driven descriptive choropleth mapping and analytical spatial interpolation techniques [16
]. ENM can produce a more accurate and robust map even with an incomplete and noisy dataset [16
]. This is the most important advantage of the ENM in disease mapping as a collection of disease data is difficult due to underreporting, misdiagnosis, and ethical issues of personal identity and is costly and labor-intensive. Further, being non-parametric ENM are capable on describing nonlinear relationships between variables [9
] to assess the interaction of spatial process of disease.
In the context of disease mapping, the aim is to determine habitat suitability for the persistence of a given disease agent and its transmission vectors at sufficient levels to result in human cases [16
]. Generating disease maps from occurrence point data is thus similar to estimating species distributions, which characterize habitats suitable for a given species. Several algorithms are available to implement ENM, among which machine learning algorithms such as MaxEnt [22
], and random forest (RF) [23
] are superior due to their higher predictive accuracy.
Despite emerging evidence, the details of spatial distributions environmental suitability of scrub typhus transmission and associated environmental factors remain unclear in Nepal. Only a few studies have attempted to assess the environmental factors associated with occurrence and spread of scrub typhus [24
]. However, no mapping efforts have been made yet to assess spatial variation of environmental suitable areas of scrub typhus transmission and associated factors in Nepal. In this study, we mapped the environmental suitability of scrub typhus and estimated human population under the risk of disease transmission in Nepal using the ecological niche approach and machine learning modeling techniques.
We fitted 100 models, 50 models in each technique based on 10-fold cross-validation. Both modeling techniques performed well with mean AUC value above 0.8 and mean TSS value above 0.6 in both training and test dataset simulations, indicating the robustness of the fitted models (Table 2
). We observed insignificant variations in AUC and TSS between the training and test split.
Among the finally selected 16 geographic and environmental variables proximity to cropland, elevation, and slope and distance to urban land were the major contributors in both models, although rank of importance was little different depending on the modeling techniques (Figure 4
The negative association of proximity to cropland and proximity to urban land to the probability of occurrence of scrub typhus is observed for Nepal. The marginal response curve for proximity to cropland decreases sharply until the value reaches 800 m and no response is observed from then (Figure 5
). However, the response curve of proximity to urban land is gentler and goes up to 1500 m.
The marginal response of elevation is positive until the height reaches around 200 m and then it becomes negative. The negative association part of the curve is more smooth and has a response up to 6000 m asl. The risk of scrub typhus initially increases with an increase in rainfall but decreases gradually once rainfall reaches 43 mm.
depicts the spatial distribution of environmentally suitable areas of scrub typhus in Nepal based on MaxEnt, RF, and ensemble techniques where suitability values range from 0 (low) to 1 (high). In general, there is a broad consistency in both methods. Despite little variation in the prediction, both models are strongly correlated with Pearson correlation values higher (r = 0.8, p
< 0.05) than when model predictions were validated based on 10,000 randomly generated sample points. The predicted high suitable areas are mainly distributed in lowland tarai and less elevated hill regions across the country in both models. The highly suitable areas are continuously distributed in the western tarai and the lower hills of central Nepal while it is irregular in the east, mainly in the southern region of east tarai.
The results show that about 43% of the population of Nepal are currently living in the highly suitable areas of scrub typhus transmission in Nepal (Table 3
) while 29% and 21% are living in suitable and moderately suitable areas. Only 6% of the population are living in the areas of environmental unsuitability for scrub typhus transmission.
Scrub typhus has been a major public health problem in Nepal since a 2015 outbreak across the country. About 3000 cases with 26 deaths were recorded in the last four years since its first outbreak [7
]. The number of scrub typhus reported districts have also increased from 16 to 60 in the same period, showing a rapid geographic expansion of scrub typhus in Nepal [25
]. In the context of rising incidences and expanding geographic distribution, scrub typhus is expected to replace typhoid as a common cause of febrile illness in Nepal [44
]. This study assessed the spatial distribution of environmentally suitable areas of scrub typhus in Nepal using the ecological niche modeling approach, machine learning techniques along with reported cases of scrub typhus and a set of environmental and geographic explanatory variables. Further, this study also estimated the population living under different levels of risk. Our results revealed that both modeling techniques, in general, could be useful in identifying environmental factors and quantifying the areas susceptible to scrub typhus outbreak. However, ensemble prediction is more comprehensive and reduces prediction uncertainties compared to the single algorithms [45
]. The spatial distribution patterns of environmentally suitable areas of scrub typhus disease were found to be largely influenced by the interaction of several environmental factors, including proximity to cropland, proximity to urban land, slope, and elevation.
Our study shows that environmentally suitable areas of scrub typhus are widely distributed in Nepal mainly in the lowland Tarai and less elevated areas in mid-hills and mountains. These areas are major population concentration areas of Nepal (Figure 7
). As a result, a significantly higher proportion of the population is under the risk of scrub typhus transmission despite a lower proportion of highly suitable areas compared to the total area of the country (Table 3
). Previous studies also claimed these regions are high-risk areas of scrub typhus [7
]. However, this is the first attempt that quantifies and maps its distribution in Nepal. Concurrent with these findings, about 81% of total reported cases were from lowland Tarai [7
]. Similar to Nepal, subtropical southern districts of Bhutan have a higher risk of scrub typhus compared to the high mountain region in the north, although few cases have been recorded from throughout the country [47
]. This indicates that lowland has a higher risk of scrub typhus; however, it might also occur in higher elevated mountains and hills.
The spatial patterns of environmentally suitable areas are continuous in the west Tarai while it is irregular and patchy in the east. Unlike the west, our model predicts southern east Tarai as less suitable for scrub typhus. The possible reasons could be the lower elevation and gentler slope of this region. As a result, such areas remain wet and waterlogged for most part of the year, not favoring the growth and proliferation of the rodent population. The recent reports also showed a higher prevalence of scrub typhus in the west Tarai including Kailali and Kanchanpur districts and central hills, including Palpa, Syangja, and Tanahu districts [48
Concurrent with previous studies, we found an elevated risk of scrub in the vicinity of cropland [14
]. In line with our findings, the mosaics of cropland and vegetation are positively associated with the risk of scrub typhus in Taiwan [11
]. The reason could be the availability of plenty of food essential for the proliferation of rodent hosts. A number of previous research have shown a positive association of scrub typhus with rodent density [10
]. The reason could also be due to higher occupational exposure of farmers working in the cropland [49
]. Previous studies also found that farmers work near the grassland scrubby vegetation and thus have higher chances of acquiring scrub typhus [25
We found an inverse association between the probability of occurrence of scrub typhus and elevation in general; however, the association was positive up to around 200 m. Similar to our findings, the risk of scrub typhus incidence increases with an increase in elevation [11
] in Taiwan. The possible reason of the inverse association might be the low temperatures in higher elevations. A decreasing trend of the proportion of cropland with an increasing altitudinal gradient in Nepal [50
] might be another reason for the inverse association of elevation and probability of occurrence of scrub typhus.
Slope is another important environmental factor of scrub typhus. Overall, there is a negative association between slope and the probability of scrub typhus occurrence. This association can again be explained by a decreasing trend of cropland with increasing slope gradients in Nepal. However, the association is much smoother in MaxEnt compared to the RF and BRT model.
Although previous studies have suspected some association of scrub typhus with the proximity to earthquake epicenters [2
] as the first worst outbreak occurred in Nepal immediately after the Gorkha earthquake of April 2015. The speculation was based on possible intimate contact between humans and rats that might have come out of their usual underground habitat after the earthquake. However, we did not find a significant role of proximity to earthquake epicenter with the occurrence of scrub typhus.
Scrub typhus is generally a rural disease [2
], and rural settings provide conducive environments for growth and proliferation of host and pathogen. However, in recent years, many urban cases have been reported from different countries including South Korea, Taiwan, and China [30
]. In Nepal, urban cases of scrub typhus are also increasingly being reported [2
]. The U-shaped marginal response curve of proximity to urban areas in the MaxEnt model indicates an elevated risk near the urban areas and far from the urban areas. All these indicate that the disease is no longer a rural disease and outbreak may occur in both rural and urban settings. Urban scrub typhus may have a significant impact because of the large population in urban areas.
Based on previous research findings [14
], we included three NDVI layers of -minium, mean, and maximum- as potential predictors of scrub typhus distribution in Nepal. However, we did not find a strong predictive role of these variables in the occurrence of scrub typhus. Similarly, unlike previous studies, the role of climate factors was less important [14
]. The possible reason could be due to the absence of proximity of variables in previous research or different ecological settings.
This study has some strengths as well as limitations. This is the first spatially explicit scrub typhus research from Nepal, which has identified environmental risk factors and mapped the spatial distribution of disease transmission risk. The findings may help to close the knowledge gap on the spatial epidemiology of scrub typhus. The concerned health authority, including the Department of Epidemiology and Disease Control Division (EDCD), could use these findings to improve surveillance and control efforts targeting more locations that are predicted as the potential scrub typhus areas. However, due to the absence of complete disease data, we were unable to explore the data-driven exploratory analysis. Future studies are encouraged to focus on an exploratory analysis in one and the validation of filed data on the other.