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One-year delayed effect of fog on malaria transmission: a time-series analysis in the rain forest area of Mengla County, south-west China

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Research
One-year delayed effect of fog on malaria transmission: a time-series
analysis in the rain forest area of Mengla County, south-west China
Linwei Tian* 1 , Yan Bi* 2 , Suzanne C Ho1,3 , Wenjie Liu4 , Song Liang5 ,
William B Goggins6 , Emily YY Chan3 , Shuisen Zhou7 and Joseph JY Sung1
1Stanley Ho Center for Emerging Infectious Diseases, School of Public
Health, Chinese University of Hong Kong, Hong Kong, PR China
2Yunnan Province Center for Disease Control and Prevention, Kunming, PR
China
3Department of Community and Family Medicine, School of Public Health,
Chinese University of Hong Kong, Hong Kong, PR China
4Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences,
Kunming, PR China
5College of Public Health, Ohio State University, Columbus, Ohio, USA
6Division of Biostatistics, School of Public Health, Chinese University of
Hong Kong, Hong Kong, PR China
7National Malaria Office, National Institute for Parasitic Diseases,
Shanghai, PR China
author email corresponding author email* Contributed equally
Malaria Journal 2008, 7:110doi:10.1186/1475-2875-7-110
The electronic version of this article is the complete one and can be
found online at: http://www.malariajournal.com/content/7/1/110
Received:27 April 2008
Accepted:19 June 2008
Published:19 June 2008

© 2008 Tian et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work is properly cited.
Abstract
Background
Malaria is a major public health burden in the tropics with the potential
to significantly increase in response to climate change. Analyses of data
from the recent past can elucidate how short-term variations in weather
factors affect malaria transmission. This study explored the impact of
climate variability on the transmission of malaria in the tropical rain
forest area of Mengla County, south-west China.
Methods
Ecological time-series analysis was performed on data collected between
1971 and 1999. Auto-regressive integrated moving average (ARIMA) models
were used to evaluate the relationship between weather factors and malaria
incidence.
Results
At the time scale of months, the predictors for malaria incidence
included: minimum temperature, maximum temperature, and fog day frequency.
The effect of minimum temperature on malaria incidence was greater in the
cool months than in the hot months. The fog day frequency in October had a
positive effect on malaria incidence in May of the following year. At the
time scale of years, the annual fog day frequency was the only weather
predictor of the annual incidence of malaria.
Conclusion
Fog day frequency was for the first time found to be a predictor of
malaria incidence in a rain forest area. The one-year delayed effect of
fog on malaria transmission may involve providing water input and
maintaining aquatic breeding sites for mosquitoes in vulnerable times when
there is little rainfall in the 6-month dry seasons. These findings should
be considered in the prediction of future patterns of malaria for similar
tropical rain forest areas worldwide.
Background
Malaria is a major public health burden in the tropics [1] with the
potential to significantly increase in response to climate change [2].
Analyses of data from the recent past can elucidate how short-term
variations in weather factors affect malaria transmission. These findings
can be applied in a modeling exercise to estimate future patterns of
malaria. Over the past century the world has warmed by 0.6°C [3], with a
range of ecological consequences [4]. The possible linkage between global
warming and the increase in malaria incidence or its geographic spread has
been extensively debated [5-7]. The current evidence is insufficient to
clearly attribute the increase of malaria incidence or its geographic
spread in the east African highlands to local warming [8]. The
relationship between climate and malaria may be highly dependent upon
local scale parameters, and it is not always possible to extrapolate the
relationship to a broader spatial scale. Moreover, caution is needed when
the empirical evidence of short-term climate variation and malaria
transmission is applied to the estimation of future impacts of climate
change. Investigations that examine the consistency of climate and malaria
relationships in different societal and regional contexts can improve our
understanding of the linkages between climate and malaria transmission and
provide a stronger scientific foundation for predicting future patterns of
malaria [9].
Although the linkage between climate variability and malaria transmission
has been widely studied in the east African highlands and other areas
[6,10-14], few studies in this regard have been conducted in the tropical
areas of southern China and south-east Asia. In this study, the potential
impact of climate variability on the transmission of malaria in a tropical
county of China was examined. Malaria is still a major public health issue
in China, especially in Yunnan and Hainan provinces, despite nationwide
malaria control efforts in the past decades [15]. In 2005, malaria
incidence was 49.5/100,000 in Yunnan Province, where a total of 15,072
cases and 38 deaths were reported. The ratio of Plasmodium vivax malaria
cases to Plasmodium falciparum malaria cases was 4:1. Mengla County
(21°09'-22°24'N, 101°05'-101°50'E) of Yunnan Province is situated just
south of the tropic of Cancer, bordering Laos on the east, south, and
south-west, and Myanmar on the west (Figure 1). It has an area of 7,093
km2, is mostly mountainous, and has a population of 0.2 million. Its
elevation ranges from 480 m to 2,023 m. Mengla County has one of the
highest malaria incidence rates in China; during 1994–1998, its annual
malaria incidence rate, 400.4/100,000, was the sixth among the 2,353
counties of China [16].
Figure 1. Location of Mengla County, China.
The purpose of the current study was to examine the effects of weather
factors on the transmission of malaria in Mengla County by using
auto-regressive integrated moving average (ARIMA) models. Ecological
time-series analysis has been used extensively to study the effect of
climate variability on infectious diseases [12,17,18]. ARIMA models are
useful tools to analyze time-series data containing ordinary or seasonal
trends [19]. The current analysis was based on malaria incidence and
weather factor data from Mengla County for the 1971–1999 period. The
weather factors included minimum temperature, maximum temperature,
rainfall, humidity, and fog. The monthly or annual fog day frequency was
used as an index variable for fog abundance.
Methods
Malaria incidence data were obtained from Yunnan Province's Center for
Disease Control and Prevention. As a national malaria surveillance
location, Mengla County has kept complete malaria records for nearly four
decades. Plasmodium vivax malaria is predominant in this county, but P.
falciparum infections also exist. Overall malaria incidence was used in
this study. All residents in the county during the 1971–1999 period were
treated as the study population. Weather data including monthly rainfall,
minimum temperature, maximum temperature, relative humidity, and fog day
frequency were retrieved from the Yunnan Bureau of Meteorology. A fog day
is defined as a day when visibility is 1,000 m or less for more than 15
min. Before conducting the time-series analysis, logarithmic
transformation was applied to the malaria incidence time series to assure
the normality and homogeneity of variance of the residuals.
ARIMA models were used to evaluate the relationship between weather
factors and monthly malaria incidence. An ARIMA model was fit first to the
predictor variable. The model was then applied to the dependent variable
before the two series were cross-correlated to determine whether an
association exists. Modeling with ARIMA involves the estimation of a
series of parameters to account for the inherent dynamics in the time
series, including the trends and autoregressive and moving average
processes. The general model introduced by Box and Jenkins [19] includes
autoregressive and moving average parameters, and explicitly includes
differencing in the formulation of the model. An ARIMA (p, d, q) model
comprises three types of parameters: the autoregressive parameters (p),
number of differencing passes (d), and moving average parameters (q). The
multiplicative seasonal ARIMA (p, d, q)(P, D, Q)s model is an extension of
the ARIMA method to time series in which a pattern repeats seasonally over
time. Analogous to the simple ARIMA parameters, the seasonal parameters
are: seasonal autoregressive (P), seasonal differencing (D), and seasonal
moving average parameters (Q). The length of the seasonal period is
represented by s.
Each of the weather input series, at lags of one to 12 months,
respectively, was fitted into the seasonal ARIMA model of monthly malaria
incidence to screen for potential weather predictors of malaria incidence.
Those input series significantly associated with malaria incidence, with a
p-value of less than 0.10, were singled out to fit the best multivariate
ARIMA model. The Ljung-Box Q test was applied to ascertain whether the
residual series were white noise. The conditional least squares method was
applied in the ARIMA procedure of SAS (SAS Institute, Inc., Cary, North
Carolina). The selection of ARIMA processes was conducted using Akaike's
information criterion (AIC), which measures how well the model fits the
series. At the time scales of years, each of the weather input series, at
lags of 0 and 1 year, respectively, was fitted into the ARIMA model of
annual malaria incidence.
In order to examine whether the association between weather and malaria
remains constant or whether it is particularly strong in certain months,
the response and predictor time series were also modeled separately and
the cross-correlations between their residual series were examined
subsequently [20,21]. One property of a white noise series (i.e., residual
series after ARIMA modeling) is that a time series composed of sequences
of a white noise series is again a white noise series [21]. This study
took advantage of this property to examine in more detail the associations
between weather factors and malaria incidence. Specific sequences (e.g.,
January of each year) of the residual time series constitute a new white
noise series. Moving a one-month time frame throughout the year can result
in 12 cross-correlations, which can be used to examine whether the
association between weather and malaria remains constant or whether it is
particularly strong in certain months. All analyses were performed using
SAS for Windows, version 9, software (SAS Institute, Inc., Cary, North
Carolina).
Results
Figure 2 shows the average seasonal pattern of rainfall, fog day
frequency, and malaria incidence in Mengla County. In this tropical rain
forest area, there are three seasons: rainy season (May-October), dry-cool
season (November-February), and dry-hot season (March-April). Seasonal
variations are apparent in malaria incidence and the two weather
variables: rainfall and fog day frequency. Rainfall is the highest in the
rainy season and lowest in the dry-cool season. Fog persists in the
dry-cool season and the dry-hot season, and occurs only occasionally in
the rainy season. Besides the rainy season peak (in July and August) of
malaria incidence, there is also a peak in November and December in the
dry-cool season.
Figure 2. Average monthly weather measurements and malaria incidence rates
(1971–1999) in Mengla County.
By fitting each of the weather input series, at lags of one month to 12
months, respectively, in the ARIMA model of monthly malaria incidence, a
total of six input series were found to be significantly associated with
malaria. Two input series, humidity and rainfall at a four-month lag, are
inversely associated with malaria incidence. The four input series
positively associated with malaria include: maximum temperature at a lag
of four months, minimum temperatures at lags of one month and two months,
and the fog day frequency at a lag of seven months. Different combinations
of monthly temperatures, rainfall, humidity, and fog were added to the
models as input series. Of all the models tested, the seasonal ARIMA
(1,1,1)(0,1,1)12 model for malaria incidence fits the data best according
to AIC and goodness-of-fit criteria.
Model II in Table 1 is the best fitting one among three models tested
based on the monthly time series data. The local moving average parameter
is 0.517, the seasonal moving average is 0.795, and the autoregression is
-0.167, all of which are statistically significant. Malaria incidence is
positively associated with maximum temperature at a lag of four months (β
= 0.018, p = 0.021), minimum temperature at a lag of one month (β = 0.032,
p = 0.002) and two months (β = 0.028, p = 0.006), and the fog day
frequency at a lag of seven months (β = 0.004, p = 0.021). Model I shows
that the inclusion of the additional covariates of humidity (β = -0.003, p
= 0.531) and rainfall (β = -0.0001, p = 0.478) does not improve the model
fit, and that these two variables are not significantly associated with
malaria incidence. In summary, the best fitting model includes minimum
temperature, maximum temperature, and fog day frequency as the predicting
variables for the monthly malaria incidence.
Table 1. ARIMA regression of the logarithmic monthly malaria incidence
(1971–1999) on the weather factors in Mengla, China
Figure 3 shows that the strength of the association between these weather
factors and malaria incidence is not constant throughout the year. The
cross-correlation between malaria incidence and minimum temperature at a
lag of one month is the strongest in December (rho = 0.575, p = 0.001). A
higher minimum temperature in November predicts an elevated malaria
incidence in December. For minimum temperature at a lag of two months, the
cross-correlation is the highest in February (rho = 0.357, p = 0.067). A
minimum temperature in December is positively associated with malaria
incidence in February of the following year. Taken together, the effect of
minimum temperature on malaria incidence appears stronger in the cool
months than in the hot months.
Figure 3. Cross-correlations between weather factors and malaria incidence
based on moving time frames of one month after modeling with the
autoregressive integrated moving average, in Mengla County from 1971 to
1999. (* p < 0.05)
The cross-correlation between malaria and maximum temperature at a lag of
four months is particularly strong in April and June (rho = 0.410, p =
0.034; rho = 0.429, p = 0.023, respectively). The maximum temperature in
the cool months is positively associated with malaria incidence after a
four-month interval. In other months, the cross-correlation is not
statistically significant. To generalize, the effect of maximum
temperature in cool months is stronger than that in hot months.
The overall association between malaria incidence and fog at a lag of
seven months is driven particularly by the cross-correlation in May (rho =
0.470, p = 0.013). The cross-correlation is not statistically significant
in other months. The fog day frequency in October is positively associated
with malaria incidence in May of the following year. The seven-month lead
time goes beyond the six-month dry season. When rainfall reduces by the
end of the rainy season, fog day frequency starts to increase. It is
actually the fog day frequency in the late rainy season that affects
malaria incidence in the early rainy season of the following year.
At the time scale of years, the 29-year series (Figure 4) were used to
identify an ARIMA (1,1,0) model for the annual malaria incidence (Table
2). The only weather variable associated with annual malaria incidence is
the fog day frequency with a lead time of one year (β = 0.003, p = 0.008).
The regressive forecast chart indicates that the predicted value and the
actual incidence of annual malaria incidence matched reasonably well. The
incidence of malaria from 1998 to 1999 was theoretically predicted by the
model and validated by the actual values (Figure 5).
Figure 4. Weather factors and malaria incidence in Mengla County,
1971–1999.Figure 5. Regressive forecasts of annual malaria incidence in
Mengla, 1971–1999.Table 2. ARIMA regression of the logarithmic annual
malaria incidence (1971–1999) on the fog day frequency in Mengla, China
Discussion
Seasonal dependence (seasonality) is apparent in the time series of
malaria incidence in this tropical rain forest study area. Besides the
major incidence peak in the rainy season, there is also a minor peak in
the dry-cool season. A similar bimodal annual pattern of malaria is seen
in the Limbe River valley of northern Haiti, with two periods of high
incidence in June and July in summer and December and January in winter
[22]. Seasonality is one type of autocorrelation, which refers to the
correlation of a time series with its own past and future values.
Cross-correlations are correlations between two time series shifted in
time relative to one another. In the presence of autocorrelation in the
individual series, the estimated cross-correlation function may be
distorted [23]. Prior to the examination of interdependency between the
climate and malaria time series, seasonal autocorrelation has to be
removed by prewhitening. Successful modeling using a seasonal ARIMA model
in this study was the basis for the examination of the effects of climate
variability on malaria transmission.
This study fails to find an association between relative humidity and
malaria incidence in the tropical rain forest area of Mengla County,
China. Provided that relative humidity has a linear dose-response effect
on fluctuations in malaria incidence, this effect would emerge also in
fluctuations in the monthly data after filtering out the seasonal
component. A high relative humidity lengthens the life of the mosquito and
helps the parasite to complete the necessary life cycle so that it can
transmit the infection. When the relative humidity drops below 60%, it is
believed that malaria transmission cannot occur because of the reduced
lifespan of mosquitoes [24]. The relative humidity throughout the year
ranges from 69% to 93% (Figure 2), so the relative humidity is probably
not a limiting factor for malaria transmission in this tropical rain
forest area.
Although rainfall generally increases the number of breeding places for
mosquitoes, the current study failed to show rainfall as a precipitating
factor for malaria transmission. This is consistent with a few studies in
the literature [10,25-27], which find a negative or neutral effect of
rainfall. Provided that rainfall has a linear dose-response effect on
fluctuations in malaria incidence, this effect would emerge after
filtering out the seasonal component. The inconsistent relationship
between rainfall and malaria incidence could result from the saturating
effect of rainfall, for an increase in rainfall fails to produce
additional malaria cases when aquatic breeding sites are not limiting for
mosquitoes [28]. In addition, heavy rainfall or storms may destroy
existing breeding places, interrupt the development of mosquito eggs or
larvae, or simply flush the eggs or larvae out of the pools.
Both minimum temperature and maximum temperature were positively
associated with malaria incidence in the study area. This finding agrees
with the finding of other field studies [6,7,12,14,28-30] in which
temperature is reported to be a precipitating factor for malaria
transmission. This is biologically plausible because temperature affects
three aspects of malaria transmission: 1) the survival and reproduction
rates of Anopheles; 2) the intensity, particularly the biting rate, of
Anopheles activity; and 3) the development, survival, and reproduction
rates of the Plasmodium within Anopheles.
The lead time between minimum temperature and malaria incidence is one to
two months in this area. This is similar to the estimate of a seven- to
ten-week lead time for the cold regions and nine- to ten-week lead time
for the warmer regions of Ethiopia [28]. In another county of southern
China, minimum temperature affects malaria incidence with a one-month
lagged effect [12]. The detection of a positive effect of maximum
temperature at a long lag of four months is not explainable, to our
knowledge, on biological grounds.
The association between minimum temperature and malaria incidence is not
constant throughout the year. It appears stronger in the cool months than
in the hot months. The one-month delayed effect of minimum temperature on
malaria is particularly strong in December, and the two-month delayed
effect of minimum temperature on malaria is particularly strong in
February. It is likely that temperature is a more important limiting
factor for malaria transmission in the cool months than in the hot months.
This temporal differentiation of the temperature effect is analogous to
reports that temperature increase has a greater effect on malaria
transmission in cool areas than in warm areas [28,29].
The fog day frequency in one month is associated with malaria incidence
with a seven-month delayed effect. The overall association is driven
particularly by the cross-correlation between fog frequency in October and
malaria incidence in May of the following year. This is the first time
that the effect of fog on malaria transmission has been reported in the
literature. The possibility cannot be ruled out that this is a spurious
finding since multiple lags (one to 12 months) were tested, but this
association is biologically plausible. Moreover, the delayed effect of fog
frequency on malaria was also revealed at the time scale of years.
Fog precipitation is an important water input in many mountainous and
coastal environments. The positive effect of fog on malaria transmission
may involve providing water input and maintaining aquatic breeding sites
for mosquitoes in vulnerable times when there is little rainfall in the
six-month dry season (November-April). The study area is located in a
tropical seasonal rain forest region in south-west China, where the daily
fog drip amount is 0.38 mm on average [31]. Radiation fog forms during the
night when cooling caused by long-wave radiation lowers the air
temperature to or below the dew point. In the dry-cool and dry-hot seasons
of this rain forest, fog drip represents up to 49% and 33%, respectively,
of the total precipitation (rainfall and fog) [31]. The dry-cool season
and the dry-hot season have average daily fog duration of 11 and 9 h,
respectively, while the rainy season has 6 h [32].
The lead time from fog events and malaria is seven months at the time
scale of months, and one-year at the time scale of years. The delayed
effect is mainly driven by the association between fog events in October
and malaria incidence in May of the following year. The lack of an
immediate effect of the fog events may be due to the negative correlation
of fog frequency and concurrent minimum temperatures. Radiation fog forms
only when the air temperature reaches or falls below the dew point. Fog
precipitation may contribute to maintaining aquatic breeding sites for
mosquitoes in cool months, but its effect on malaria transmission emerges
only when the temperature and humidity are also optimum for malaria
transmission. The delayed effect of fog events on malaria transmission may
also involve the interaction of other hydrology and ecology factors in
this tropical seasonal rain forest area.
The current study has two limitations. First, P. vivax and P. falciparum
malaria cases were pooled together when malaria incidence was calculated.
Detailed data on each Plasmodium were not available for the study period.
The ratio of P. vivax malaria cases to P. falciparum malaria cases was
roughly 4:1. Second, potential confounding factors, such as land cover
change and public health intervention measures, may have influenced
malaria incidence and may have been associated with the weather factors
examined in this study. Because of the lack of historical data, these
factors were not adjusted for in the regression modeling.
Conclusion
Minimum temperature, maximum temperature, and fog day frequency are
predictors for malaria incidence in the tropical seasonal rain forest area
of Mengla County, China. The effect of minimum temperature on malaria
incidence is greater in the cool months than in the hot months. The fog
day frequency in October has a positive effect on malaria incidence in May
of the following year. On the time scale of years, fog day frequency has a
one-year delayed effect on the annual incidence of malaria. These findings
should be considered in the prediction of future patterns of malaria for
similar tropical rain forest areas worldwide.
Authors' contributions
LWT and YB conceived the study, undertook statistical analysis and drafted
the manuscript, SCH, EYYC and JJYS made major contributions to the study
design and statistical analysis, WJL and SSZ initiated the study and made
weather data and malaria incidence data available, SL and WBG participated
in the statistical analysis and helped to draft the manuscript. All
authors contributed to the writing of the manuscript and approved the
submitted version of the manuscript.
Acknowledgements
WJL was funded by the National Natural Science Foundation of China (grant
numbers 30770368 and 30570308). Financial support for data collection was
provided by the Stanley Ho Centre for Emerging Infectious Diseases,
Chinese University of Hong Kong. We thank Hong Qiu and Ignatius Yu for
helpful discussions and comments. YB is currently a student at the Centre
for Environment and Population Health, Griffith University, Australia
(from January to December, 2008), supported by Australian Leadership Award
Fellowships (ALAF) on the Chinese Disease Control and Prevention
Leadership Program. We thank the following people for their help in data
collection: Shengliang Wang from the Centre for Disease Control and
Prevention of Xishuangbanna Prefecture; Huaxing Liu, Yujiang Xiao, Wenshan
Ge, Laifa Zhao and Jinfang Cheng from the Centre for Disease Control and
Prevention of Mengla County, China.
References
Sachs J, Malaney P: The economic and social burden of malaria.
Nature 2002, 415(6972):680-685. PubMed Abstract | Publisher Full Text
Return to text
Patz JA, Olson SH: Malaria risk and temperature: Influences from global
climate change and local land use practices.
Proc Natl Acad Sci USA 2006, 103(15):5635-5636. PubMed Abstract |
Publisher Full Text | PubMed Central Full Text
Return to text
Houghton JT, Ding Y, Griggs DJ, Noguer M, Linden PJvd, Dai X, Maskell K,
Johnson CA: Climate Change 2001: The Scientific Basis: Contribution of
Working Group I to the Third Assessment Report of the Intergovernmental
Panel on Climate Change. Cambridge University Press; 2001.
Return to text
Walther GR, Post E, Convey P, Menzel A, Parmesan C, Beebee TJC,
Fromentin JM, Hoegh-Guldberg O, Bairlein F: Ecological responses to
recent climate change.
Nature 2002, 416(6879):389-395. PubMed Abstract | Publisher Full Text
Return to text
Hay SI, Cox J, Rogers DJ, Randolph SE, Stern DI, Shanks GD, Myers MF,
Snow RW: Climate change and the resurgence of malaria in the East
African highlands.
Nature 2002, 415(6874):905-909. PubMed Abstract | Publisher Full Text
Return to text
Zhou GF, Minakawa N, Githeko AK, Yan GY: Climate variability and malaria
epidemics in the highlands of East Africa.
Trends Parasitol 2005, 21(2):54-56. PubMed Abstract | Publisher Full
Text
Return to text
Pascual M, Ahumada JA, Chaves LF, Rodo X, Bouma M: Malaria resurgence in
the East African highlands: Temperature trends revisited.
Proc Natl Acad Sci USA 2006, 103(15):5829-5834. PubMed Abstract |
Publisher Full Text | PubMed Central Full Text
Return to text
McMichael AJ, Woodruff RE, Hales S: Climate change and human health:
present and future risks.
Lancet 2006, 367(9513):859-869. PubMed Abstract | Publisher Full Text
Return to text
NRC: Under the weather: climate, ecosystems, and infectious diseases.
Washington, D.C.: National Academy Press; 2001.
Return to text
Shanks GD, Hay SI, Stern DI, Biomndo K, Snow RW: Meteorologic influences
on Plasmodium falciparum malaria in the highland tea estates of Kericho,
western Kenya.
Emerg Infect Dis 2002, 8(12):1404-1408. PubMed Abstract | Publisher Full
Text
Return to text
Abeku TA, De Vlas SJ, Borsboom G, Tadege A, Gebreyesus Y, Gebreyohannes
H, Alamirew D, Seifu A, Nagelkerke NJD, Habbema JDF: Effects of
meteorological factors on epidemic malaria in Ethiopia: a statistical
modelling approach based on theoretical reasoning.
Parasitology 2004, 128(Pt 6):585-593. PubMed Abstract | Publisher Full
Text
Return to text
Bi P, Tong S, Donald K, Parton KA, Ni J: Climatic variables and
transmission of malaria: a 12-year data analysis in Shuchen County,
China.
Public Health Rep 2003, 118(1):65-71. PubMed Abstract | PubMed Central
Full Text
Return to text
Minakawa N, Omukunda E, Zhou GF, Githeko A, Yan GY: Malaria vector
productivity in relation to the highland environment in Kenya.
Am J Trop Med Hyg 2006, 75(3):448-453. PubMed Abstract | Publisher Full
Text
Return to text
Ye Y, Louis VR, Simboro S, Sauerborn R: Effect of meteorological factors
on clinical malaria risk among children: an assessment using
village-based meteorological stations and community-based
parasitological survey.
BMC Public Health 2007, 7:101. PubMed Abstract | BioMed Central Full
Text | PubMed Central Full Text
Return to text
Zhou SS, Wang Y, Tang LH: Malaria situation in the People's Republic of
China in 2005.
Chinese Journal of Parasitology and Parasitic Diseases 2006, 24:401-403.

Return to text
Gao CY, Chai GJ, Han GH, Yang XW, Liu L, Jiang ZJ: Time trend analysis
of malaria incidence in China: 1950–2001.
Chinese Journal of Public Health 2003, 19:725-726.
Return to text
Tong S, Hu W: Climate variation and incidence of Ross river virus in
Cairns, Australia: a time-series analysis.
Environ Health Perspect 2001, 109(12):1271-1273. PubMed Abstract |
Publisher Full Text | PubMed Central Full Text
Return to text
Abeku TA, de Vlas SJ, Borsboom G, Teklehaimanot A, Kebede A, Olana D,
van Oortmarssen GJ, Habbema JDF: Forecasting malaria incidence from
historical morbidity patterns in epidemic-prone areas of Ethiopia: a
simple seasonal adjustment method performs best.
Trop Med Int Health 2002, 7(10):851-857. PubMed Abstract | Publisher
Full Text
Return to text
Box GEP, Jenkins GM: Time series analysis: forecasting and control. San
Francisco, CA: Holden-Day; 1976.
Return to text
Helfenstein U: Detecting Hidden Relations between Time-Series of
Mortality-Rates.
Methods Inf Med 1990, 29(1):57-60. PubMed Abstract
Return to text
Ajdacic-Gross V, Lauber C, Sansossio R, Bopp M, Eich D, Gostynski M,
Gutzwiller F, Rossler W: Seasonal associations between weather
conditions and suicide – Evidence against a classic hypothesis.
Am J Epidemiol 2007, 165(5):561-569. PubMed Abstract | Publisher Full
Text
Return to text
Vanderwal T, Paulton R: Malaria in the Limbe River valley of northern
Haiti: a hospital-based retrospective study, 1975–1997.
Rev Panam Salud Publica 2000, 7(3):162-167. PubMed Abstract | Publisher
Full Text
Return to text
Diggle PJ: Time series. A biostatistical introduction. Oxford, United
Kindom: Clarendon Press; 1990.
Return to text
Pampana E: A textbook of malaria eradication. London: Oxford University
Press; 1969.
Return to text
Lindsay SW, Bodker R, Malima R, Msangeni HA, Kisinza W: Effect of
1997–98 El Nino on highland malaria in Tanzania.
Lancet 2000, 355(9208):989-990. PubMed Abstract | Publisher Full Text
Return to text
Singh N, Sharma VP: Patterns of rainfall and malaria in Madhya Pradesh,
central India.
Ann Trop Med Parasitol 2002, 96(4):349-359. PubMed Abstract | Publisher
Full Text
Return to text
Abeku TA, van Oortmarssen GJ, Borsboom G, de Vlas SJ, Habbema JD:
Spatial and temporal variations of malaria epidemic risk in Ethiopia:
factors involved and implications.
Acta Trop 2003, 87(3):331-340. PubMed Abstract | Publisher Full Text
Return to text
Teklehaimanot HD, Lipsitch M, Teklehaimanot A, Schwartz J: Weather-based
prediction of Plasmodium falciparum malaria in epidemic-prone regions of
Ethiopia I. Patterns of lagged weather effects reflect biological
mechanisms.
Malaria Journal 2004, 3:41. PubMed Abstract | BioMed Central Full Text |
PubMed Central Full Text
Return to text
Lindsay SW, Birley MH: Climate change and malaria transmission.
Ann Trop Med Parasitol 1996, 90(6):573-588. PubMed Abstract
Return to text
Kleinschmidt I, Sharp BL, Clarke GPY, Curtis B, Fraser C: Use of
generalized linear mixed models in the spatial analysis of small-area
malaria incidence rates in KwaZulu Natal, South Africa.
Am J Epidemiol 2001, 153(12):1213-1221. PubMed Abstract | Publisher Full
Text
Return to text
Liu WJ, Meng FR, Zhang YP, Liu YH, Li HM: Water input from fog drip in
the tropical seasonal rain forest of Xishuangbanna, South-West China.
J Trop Ecology 2004, 20:517-524. Publisher Full Text
Return to text
Liu WJ, Zhang YP, Li HM, Meng FR, Liu YH, Wang CM: Fog and rainwater
chemistry in the tropical seasonal rain forest of Xishuangbanna,
Southwest China.
Water Air Soil Pollut 2005, 167:295-309. Publisher Full Text
Return to text
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