Plague, a zoonosis caused by Yersinia pestis, is found in Asia and the Americas, but predominantly in Africa, with the island of Madagascar reporting almost one third of human cases worldwide. Plague' s occurrence is affected by local climate factors which in turn are influenced by large-scale climate phenomena such as the El Niño Southern Oscillation (ENSO). The effects of ENSO on regional climate are often enhanced or reduced by a second large-scale climate phenomenon, the Indian Ocean Dipole (IOD). It is known that ENSO and the IOD interact as drivers of disease. Yet the impacts of these phenomena in driving plague dynamics via their effect on regional climate, and specifically contributing to the foci of transmission on Madagascar, are unknown. Here we present the first analysis of the effects of ENSO and IOD on plague in Madagascar. We use a forty-eight year monthly time-series of reported human plague cases from 1960 to 2008. Using wavelet analysis, we show that over the last fifty years there have been complex non-stationary associations between ENSO/IOD and the dynamics of plague in Madagascar. We demonstrate that ENSO and IOD influence temperature in Madagascar and that temperature and plague cycles are associated. The effects on plague appear to be mediated more by temperature, but precipitation also undoubtedly influences plague in Madagascar. Our results confirm a relationship between plague anomalies and an increase in the intensity of ENSO events and precipitation. This work widens the understanding of how climate factors acting over different temporal scales can combine to drive local disease dynamics. Given the association of increasing ENSO strength and plague anomalies in Madagascar it may in future be possible to forecast plague outbreaks in Madagascar. The study gives insight into the complex and changing relationship between climate factors and plague in Madagascar.
Plague is a vector-borne, highly virulent zoonotic disease present today in the Americas, Asia and Africa. It is caused by infection with the bacterium Yersinia pestis, which triggers serious illness with up to seventy percent case fatality in human populations if left untreated. The infection is easily treated with antibiotics, yet these are often difficult to access in time in low income settings. The increasing frequency of the disease in many parts of the world [1], [2], [3] has been partly attributed to changes in climate [4]. Presently Africa accounts for more than ninety percent of all human plague cases reported worldwide. Within African countries, the majority of cases are reported from Madagascar and the Democratic Republic of Congo [5], with Madagascar reporting almost one third of human cases worldwide. Plague is endemic in the highland region of Madagascar and more than one hundred human cases are reported every year, though the true number of cases is likely to be higher. The reasons for such pronounced foci in these areas include extreme poverty and lack of health infrastructure, as well as unique climate features. Yersinia pestis bacteria are transmitted between rodent hosts via their fleas; humans are accidentally infected when in contact with rodent fleas or infected animal tissue. Like many vector-borne diseases, plague' s occurrence varies temporally and spatially on a variety of scales. The primary mechanisms directing this heterogeneity are thought to be driven by local variation in factors such as temperature and rainfall. For example temperature influences the rate of development of the bacterium in the flea, and the survival and development times of the fleas themselves, while precipitation affects food availability and thus fecundity of the rodent host [6], as well as having impacts on habitat characteristics and human-rodent contact (via rodent responses to flooding). Seasonal climate in many parts of the world is affected by the El Niño Southern Oscillation (ENSO), a periodic fluctuation in sea surface temperature and air pressure in the Pacific Ocean which modifies the general flow of the atmosphere in the tropics. Warm (cold) phases of the ENSO, called El Niño (La Niña), are associated with a warming (cooling) of the tropical troposphere [7]. ENSO impacts on the climate of Madagascar in a manner similar to that observed over southern Africa by Nicholson and Selato [8]. In general, an austral spring-summer El Niño starting from September causes warmer and drier conditions than usual during the austral summer-autumn four to seven months later, and cooler and wetter conditions eight to twelve months later. La Niña has the opposite effect, leading to wetter and cooler, followed by drier and warmer, conditions than average. The Indian Ocean Dipole (IOD) [9] is a periodic fluctuation in the relative sea surface temperatures of the western and eastern parts of the Indian Ocean. As the western pole of the IOD is located near to Madagascar, IOD events affect the convection locally and in turn influence the climate of the island. Thus, a positive IOD event is associated with warmer and wetter conditions over the island, while the opposite is true for a negative event [9]. We present here the first analysis of the effects of ENSO and IOD on the temporal distribution of confirmed human plague cases in Madagascar. We use a forty-eight year time-series from 1960–2008. First we identified periods of anomalous incidence and evaluated their occurrence against any coinciding trends of various climate variables. Second we use wavelets to investigate the strength and direction of association between the incidence of plague and ENSO, IOD, temperature and precipitation. Wavelet analysis allows detection of relationships between two time series in time-frequency space. Our approach uses analytical methods which allow a more thorough analysis than the usual identification of long-term unidirectional trends.
Data on all confirmed human plague cases reported in Madagascar from 1956 to 2008 were made available by the World Health Organisation Plague Reference Laboratory of the Institut Pasteur de Madagascar. A time-series of monthly incidence from 1960 to 2008 was created using the date of onset of symptoms for each confirmed bubonic, pneumonic or septicaemic case, 5-yearly human population growth estimates from the United Nations and the last population census from 1993 [10]. Incidence was calculated for each month using the number of cases, multiplying it by 100,000 for scale and dividing it by the relevant time-specific population estimate. Data from before 1960 was available but was omitted due to vaccination campaigns which ceased in 1959. For effective immunisation, people have to be re-vaccinated yearly. Malaria prevention programs, which occur in some areas of Madagascar and use indoor residual spraying of insecticide, have the potential to impact flea populations. However, this could not be quantified or corrected for. Re-calculated climate variables were downloaded via the climate explorer website [11] based on the National Center for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) reanalysis data [12] or the Centre of Environmental Data Archival website (precipitation) [13]. For the El Niño Southern Oscillation variable the index of the Japan Meteorological Agency (JMA) was retrieved from the Centre for Ocean-Atmospheric Prediction Studies website [14]. Monthly surface temperature and precipitation anomalies for the geographical area of Madagascar (42. 18°E–49. 68°E; 24. 76°S-11. 42°S) for the period 1960–2008 were used to describe seasonal cycles and in wavelet analyses. A plague incidence anomaly of >0. 1 or <−0. 1 was used to define months of anomalously high/low plague incidence respectively. When months of anomalously high or low plague incidence were sequential or less than 3 months apart, they were considered to be part of the same plague event; otherwise, they were treated as separate plague events. A plague event could therefore be for a single month or for several months. For plague events of >1 month duration, the single month which had the maximum or minimum incidence anomaly (depending on whether it was a high or low incidence event) was identified. We tested for three types of association between plague incidence anomaly and the explanatory variables: (i) using Analysis of Variance, to test for associations between positive or negative plague anomalies and the value of the explanatory variables (JMA, IOD, temperature, precipitation) in the month or peak month of a plague event; (ii) using Analysis of Variance, to test for associations between positive or negative plague anomalies and the mean value of the explanatory variable for the year centred on the month or peak month of a plague event; (iii) using Fisher Exact Test, to test for associations between positive or negative plague anomalies and positive or negative trends in the explanatory variables for the year centred on the month or peak month of a plague event. For the third analysis, we obtained the sign of the linear regression coefficient for the trend of the 12 monthly values (with the 7th month as the peak plague anomaly month) and use this to determine if the explanatory variable was tending to increase (positive coefficient), or decrease (negative coefficient), at the time of a plague event. Time-series analysis using wavelets was undertaken on ENSO/IOD/temperature/precipitation and human plague incidence anomaly datasets spanning the 48-year study period. The objective was to establish any associations in time between plague and the climate variables, and, if present, their direction and periodicity. Wavelet analysis [15], [16] is a powerful means to identify statistical relationships between signals, and is especially useful when there is non-stationarity; i. e. the periodicity changes with time [15], [17]. Wavelet analysis is a widely recognised tool to investigate temporal dynamics of infectious diseases [18], [19], [20], [21], [22], but has never been used to study the relationship between climate variables and plague in Madagascar. To detect temporal patterns, their variations and coherence, we applied wavelet analysis according to the methods of Grinsted [17] using the software R v. 2. 15 [23] and Matlab 8. 0 v. R2012a with Wavelet Toolbox [24]. The following procedure was used. First, the five variables of interest - precipitation, temperature, ENSO, IOD and plague incidence - were tested for normality, and where necessary, normalised using a Johnson transformation [25]. A low-pass Gaussian filter was used to remove the intra-seasonal variability in the time-series. Second, the stationarity or non-stationarity of each variable was determined using continuous wavelet decomposition. Each time-series was decomposed and the continuous wavelet transform plot was examined to confirm the presence of high significant variance and to establish its periodicity and any changes within the time-series. Third, the strength of any relationship between certain pairs of variables was investigated using cross-wavelet analysis, which identifies high common power between two signals (time frames where both signals vary together). The direction of the vectors reveals information about the phase relationship (i. e. time-lags) between two time-series. A vector pointing to the right indicates the time-series cycle in-phase and a vector to the left indicates cycling in anti-phase. Thus, any red/yellow areas within figures show periods during which the signals cycle with high common power with the vectors signifying the phase and the time lag. Lastly, the presence and direction of any relationship (positive/negative) between variables was established using wavelet coherency analysis, with vectors again indicating the direction of association and time lag. Here, a vector pointing to the right indicates positive association and a vector to the left indicates negative association. Downward or upward pointing vectors reveal information about which time-series leads. Wavelet coherence can be understood as an association between signals in the power spectrum space. It identifies regions of the power-space spectrum where vectors point in one direction. For both cross-wavelet and wavelet coherence analysis, information on time-lags between the time-series is revealed by the direction of the vectors. If two signals cycle with significant common power in cross-wavelet analysis or show association in wavelet coherence analysis with a 2 year periodicity, a right-pointing vector means they are cycling in phase; a left pointing vector means they are cycling in anti-phase (one lags the other by half the periodicity, i. e. 1 year); a downward pointing vector means the first signal leads the second signal by one quarter of the periodicity (half the difference between in-phase and anti-phase, i. e. 6 months; and so forth) while an upward pointing vector means the second signal leads the first signal by one quarter periodicity. A cone of influence (COI) was applied to the cross-wavelet and wavelet coherence transforms to mark the limits of the time scale within which signal behaviour can be discussed with confidence. The statistical significance level of the wavelet coherence is estimated using Monte Carlo methods [16]. Further information about these methods is provided by Grinsted et al. [17].
Plague cases occur year-round but there is strong seasonality, with most cases occurring from September to March (the austral summer) and reaching a peak from November to January (Figure 1A). The largest inter-annual variability in plague cases occurs from October to December. The seasonal cycles of temperature and rainfall show highest values from November to April, and December to March respectively (Figure 1B and 1C). December to March are also the months with the largest inter-annual range in rainfall. The inter-annual range in temperature values is approximately equal in all months. Thus, the warm, wet season is the time of the highest plague incidence and greatest variation in incidence.
It is widely accepted that ENSO and IOD are dominant drivers of earth' s year-to-year climate variability, and have wide-ranging implications for public health, including influencing the periodicity of many infectious diseases [26], [27], [28], [29], [30], [31]. This study is the first, however, to demonstrate an influence of global climate drivers on plague incidence in Madagascar. The results demonstrate a strong association between ENSO and plague in Madagascar, a country where about one third of the world' s human cases occur and there is still significant mortality from the disease. For much of the time series ENSO cycles on a 2–5 year time scale (later 2–7 years). This appears to lead to a similar periodicity (also changing with time) in the occurrence of plague. However, the association is non-stationary: early in the time series, plague leads ENSO by a few months; later they are anti-phase; and at the end of the time series they are in phase. An analysis of the climate conditions around the time of high and low plague anomalies found evidence for larger plague outbreaks being associated with increasing ENSO signal (i. e. increasing El Niño conditions). Our analyses find some association, also non-stationary, between IOD and plague. This association is less strong, however, than the association of ENSO and plague. ENSO and IOD are unlikely to directly affect plague in Madagascar. Instead they influence local climate which in turn affects the disease. In this study we demonstrate that ENSO and IOD influence temperature in Madagascar and that temperature cycles are associated with plague cycles. Furthermore, the pattern of association between ENSO and temperature in terms of changes in lag and periodicity is similar to the pattern of association between the cycles of plague and temperature. We did not find a clear effect of ENSO or IOD on precipitation; and we found only limited evidence for association between cycles of precipitation and plague. However, while ENSO and IOD may have limited influence on precipitation, we nevertheless found that larger than usual plague outbreaks were significantly associated with rainier than usual years. Hence, the effects of ENSO and IOD on plague appear to be mediated more by temperature, but precipitation also undoubtedly influences plague in Madagascar. During the late 1990s, ENSO events became progressively stronger, increasing in both frequency and amplitude. From 1976, a shift towards warmer and wetter conditions in the tropical Pacific was detected, with widespread climatic and ecological consequences [32]. Thus, changes in the association between ENSO, sea surface temperature and several diseases may be explained by their modulation by the decadal background and changes in variability. The analysis of ENSO and its association with plague incidence revealed a relationship between plague anomalies and an increase in the intensity of ENSO events and precipitation. In our study the correlation between ENSO and plague turned from weakly positive to strongly negative and finally to positive again while the association between incidence and the IOD changed from negative to positive and became stronger with time. This non-stationarity is likely caused by changes to the lags in the response of Madagascar' s climate to ENSO/IOD, or changes to the lags in the response of plague to local climatic conditions. The epidemiology of plague is sufficiently complex that the lag between climate and plague incidence may be long; as, for example, rodent and flea populations respond to favourable conditions. Thus, cycling of ENSO and plague in phase during the later part of the time-series does not necessarily imply that plague was responding immediately to ENSO. Instead, it could be that its response is an entire cycle behind (1–2 years). Equally, at first sight it seems hard to reconcile that ENSO affects plague, and yet in the early part of the time series plague leads (comes before) ENSO. It is possible, however, that plague is actually most of a cycle behind ENSO. The plague system is highly seasonal in the plague focus area which suggests that the dry and cold months from May to September are not favourable for transmission. However, there is a changing relationship between ENSO and its effects on temperature and in turn on plague. Until the 1980s ENSO affects temperature positively on the island 12 to 18 months later, while temperature is associated negatively with plague. From then onwards until the end of the time-series, the temperature response to ENSO accelerates to 8–9 months. This shift in response is most likely related to changes in the frequency and magnitude of ENSO events and a warmer decadal background as shown by the overall increase in temperature. At times ENSO and IOD may interact in their effects on plague. During the first phase of positive association between ENSO and incidence from 1960 to 1975, a period dominated by La Niña events, notable plague outbreaks are related to El Niño causing warmer than usual temperatures during a time of cooler and drier conditions usually brought by La Niña. In the second phase during the 1980s, plague is negatively associated with ENSO during a time when the temperature response to ENSO accelerates, changing the timing of an increase in temperature. Finally, from 1995 onwards, the intensity and magnitude of ENSO events increases drastically and plague shows positive correlation with ENSO again. At the same time the IOD starts impacting on plague probably via its effects on temperature 6 months later and almost immediate effects on rainfall. Together, positive ENSO and IOD events are creating warmer and wetter conditions. This is exemplified by 1997 which saw one of the largest positive plague incidence anomalies, and was the year with the strongest El Niño and positive IOD in the time-series. The intensity of ENSO and IOD events and corresponding increases in temperature, as well as increases in rainfall, show strong associations with an increase in plague incidence in Madagascar. The seasonality of plague confirms the strong link between disease transmission and optimal environmental conditions, via their effects on vector and host. Higher temperature and increased precipitation during the cold and dry season in Madagascar are likely to increase flea survival and shorten flea development time [33], [34], [35], [36]. An ENSO event influencing the temperature around this time of year would therefore create more favourable conditions for plague transmission. Changes in the climate–disease relationship over time, as detected here, have been found for certain other diseases like cholera [37], most likely modulated by long-term climate change effects and their influence on epidemiological systems. Plague incidence has also been linked to global climate in other parts of the world, and several studies have hypothesized about the potential mechanisms. Temperature thresholds exist for pathogen survival and transmission, and temperature and precipitation affect the environment of both vector and rodent host. In China, an increased rate of human plague at the province level was associated with the Southern Oscillation Index and Sea Surface Temperature of the tropic Pacific east of the equator [38]. Similarly, inter-annual variability of disease incidence in the Americas correlates with global climate phenomena, with the link hypothesised to be caused by effects on the rodent host community [39], [40], [41]. In the US the association between global climate and plague was shown to depend on time-lagged precipitation events, presumably increasing rodent populations via food availability, and on relatively cool summer temperatures during the plague transmission season, potentially increasing the abundance of infectious fleas [21]. Therefore a coherency of ENSO and plague incidence is most likely due to the influence of ENSO on temperature which in turn affects host and vector ecology and transmission potential [2], [41], [42]. There are of course other drivers for plague, which have not been considered here. These might also be related to climate effects and act on plague indirectly through anthropogenic and socioeconomic factors such as poverty, migration and cultural practises, all of which can influence disease transmission risks [43], [44], [45]. Global climate influences an array of factors which affect the epidemiology of plague, many of which will depend on both ecological and anthropogenic characteristics. The implications of a link between human plague and ENSO and IOD in Madagascar are exceedingly complex but this study leads the way to understanding the relationship between large scale climate and plague in a country where one third of the world' s cases occur.
Plague is a vector-borne bacterial infection with rodents and their fleas as its principal hosts. Transmission to humans occurs via the bite of an infected flea. In the highlands of Madagascar, plague is endemic and more than one hundred human cases are reported every year. Global climate is known to affect many infectious diseases and has been shown to affect plague incidence in other areas of the world. The ENSO and the IOD are global climate drivers affecting rainfall and temperature in Madagascar. Our study investigates the effect of global climate drivers on human plague incidence on the island. We found a link between ENSO, IOD, temperature and precipitation and plague incidence throughout the 48-year time-series although it was not constant over time. The correlation between ENSO and plague turned from weakly positive to strongly negative and then to positive, and the association with the IOD became stronger with time. We demonstrate that during periods of high ENSO intensity, plague incidence is likely to increase via ENSO' s impact on temperature and precipitation. This shows that climate indices can be a tool to help predict human plague incidence.
ecology and environmental sciences
medicine and health sciences
infectious disease epidemiology
tropical diseases
microbiology
bacterial diseases
plant science
yersinia
global health
plant pathology
neglected tropical diseases
bacterial pathogens
public and occupational health
infectious diseases
yersinia pestis
medical microbiology
epidemiology
microbial pathogens
earth sciences
biology and life sciences
A Non-Stationary Relationship between Global Climate Phenomena and Human Plague Incidence in Madagascar