Climate-Based Models for Understanding and Forecasting Dengue Epidemics

Dengue fever is a major public health problem in the tropics and subtropics. Since no vaccine exists, understanding and predicting outbreaks remain of crucial interest. Climate influences the mosquito-vector biology and the viral transmission cycle. Its impact on dengue dynamics is of growing interest. We analyzed the epidemiology of dengue in Noumea (New Caledonia) from 1971 to 2010 and its relationships with local and remote climate conditions using an original approach combining a comparison of epidemic and non epidemic years, bivariate and multivariate analyses. We found that the occurrence of outbreaks in Noumea was strongly influenced by climate during the last forty years. Efficient models were developed to estimate the yearly risk of outbreak as a function of two meteorological variables that were contemporaneous (explicative model) or prior (predictive model) to the outbreak onset. Local threshold values of maximal temperature and relative humidity were identified. Our results provide new insights to understand the link between climate and dengue outbreaks, and have a substantial impact on dengue management in New Caledonia since the health authorities have integrated these models into their decision making process and vector control policies. This raises the possibility to provide similar early warning systems in other countries

Epidemiology of dengue fever and evolution of annual mean temperature in Noumea-New Caledonia (1971–2010).

<p>The predominant circulating serotype (DENV-1, DENV-2, DENV-3 or DENV-4) is indicated in black characters. When other serotypes were detected, they are indicated in little grey characters. Annual dengue incidence rates observed in Noumea over the 1995–2010 period are highly correlated with dengue incidence rates observed in New Caledonia (Spearman coefficient <i>rho</i> = 0.99, <i>p</i>-value = 1*10⁻¹⁴). Annual dengue incidence rates in Noumea (1971–1994) were estimated (green dotted line with circles) on the basis of the relationship between incidence rates observed in New Caledonia (grey line) and those observed in Noumea (blue dotted line with crosses) using a linear model. During the 1971–2010 period, dengue incidence rates and annual mean temperatures (from January to December) were significantly correlated in Noumea (Spearman's coefficient <i>rho</i> = 0.426, <i>p</i>-value = 0.007). An increasing trend of dengue outbreaks amplitude and annual mean temperatures were observed during this 40-year study period.</p

Relationship between maximal temperatures and dengue outbreaks in Noumea.

<p>Averages and 95% confidence intervals (IC95%) of max Temp (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001470#pntd-0001470-g004" target="_blank">Figure 4a</a>) and NOD_max Temp_32 (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001470#pntd-0001470-g004" target="_blank">Figure 4b</a>) calculated monthly during epidemic and non epidemic years were compared from August (year <i>y</i>-1) to July (year <i>y</i>). The peak of max Temp preceded the epidemic peak of dengue with a lag of 1–2 months. The number of days with max Temp exceeding 32°C during the first quarter of the year was significantly higher during epidemic years than during non epidemic years, especially in February (NOD_max Temp_32_February = 7.25 versus 2 days, respectively).</p

Correlations between meteorological variables and dengue outbreaks in Noumea.

<p>Monthly and quarterly meteorological data measured from September (year <i>y</i>-1) to April (year <i>y</i>), i.e. four months before or after the outbreak onset, were analyzed from 1971 to 2010 in Noumea. For each family of meteorological variables, the three variables most correlated with the occurrence of dengue outbreaks are presented, <i>p</i>-value<0.05 indicating statistical significance.</p><p>Monthly and quarterly parameters were named “parameter_month”, and “parameter_first letter of each month of the quarter”, respectively. Number of days with a parameter over a threshold <i>x</i> were named NOD_parameter_threshold <i>x</i>.</p

Seasonal evolution of monthly entomological surveillance indices and meteorological data in Noumea (August 2000–July 2009).

<p>HI, BI and API evolution display a strong seasonal cycle, with highest values between January and July. Entomological surveillance indices were significantly correlated with meteorological data at the seasonal scale. The peak of mean Temp preceded the peak of Precip, mean RH and API with a lag of one month, and the peak of HI and BI with a lag of two months.</p

SVM predictive model of dengue outbreaks in Noumea (leave-one-out cross validation).

<p>The model estimates the probability of dengue outbreak occurrence (red bars) each year <i>y</i> according to the quarterly mean of maximal relative humidity during October–November–December (max RH_OND), and the monthly mean of maximal temperature in December (max Temp_December) year <i>y</i>-1. Results obtained in leave-one-out cross validation are presented in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001470#pntd-0001470-g007" target="_blank">Figure 7a</a>. The black line indicates the annual dengue incidence rate, and black diamonds indicate epidemic years according to the median method. The ROC curve (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001470#pntd-0001470-g007" target="_blank">Figure 7b</a>) indicates the rates of true and false positives for different detection thresholds. For example, for a probability of dengue outbreak above 65% (0.65), 11 of 20 epidemic years were predicted correctly (true positive rate = 55%) with three false alarms (false positive rate = 15%). The sensitivity of this model for this threshold is 55% (11 epidemic years predicted correctly/10 epidemic years), the specificity 85% (17 non epidemic years predicted correctly/20 non epidemic years), the positive predictive value 79% (11 epidemic years predicted correctly/14 epidemic years predicted by the model), and the negative predictive value 65% (17 non epidemic years predicted correctly/26 non epidemic years predicted by the model).</p

Scatter plots of epidemic and non epidemic years with regards to NOD_max Temp_32_JFM and NOD_max RH_95_January.

<p>Each year, the number of days with maximal temperature exceeding 32°C during January–February–March (NOD_max Temp_32_JFM) and the number of days with maximal relative humidity exceeding 95% during January (NOD_max RH_95_January) were calculated. Two methods denoted “tercile method” and “median method” were used to separate the years on the basis of annual dengue incidence rates in Noumea (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001470#s2" target="_blank">Methods</a>). On the left panel, epidemic years (dengue incidence rate in the upper tercile, i.e. >19.48 cases/10,000 inhabitants/year) and non epidemic years (dengue incidence rate in the lower tercile, i.e. <4.13 cases/10,000 inhabitants/year) are presented. The distribution of crosses (epidemic years) and circles (non epidemic years) permits the identification of three groups (A, B, C). All non epidemic years belonged to group A whereas all epidemic years, except 1973 and 2003, belonged to either group B or group C suggesting that dengue outbreaks can occur in distinct climatic conditions. On the right panel, epidemic years (dengue incidence rate greater than the median, i.e. 7.65 cases/10,000 inhabitants/year) and non epidemic years (dengue incidence rate lower than the median) are presented with the advantage of a whole set of data being usable for modelling. Years that were not considered with the tercile method (dengue incidence rate in the middle tercile) are coloured in red. Further epidemic (red crosses) and non epidemic years (red circles) are considered with the median method, and similar groups (A, B, C) were identified. With the median method, three epidemic years (1978, 1979 and 1985) and one non epidemic year (2002) were incorrectly classified. These four years were characterized by annual dengue incidence rates closed to the median.</p

SVM predictive model probability contours superimposed with max RH_OND and max Temp_December during epidemic/non epidemic years.

<p>Similarly to the SVM explicative model (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001470#pntd-0001470-g008" target="_blank">Figure 8</a>), the quarterly mean of maximal relative humidity during October–November–December (max RH_OND), and the monthly mean maximal temperature in December (max Temp_December) of the year <i>y</i>-1 were used to build the SVM predictive model.</p

Monthly distribution of laboratory positive dengue cases during epidemic and non epidemic years.

<p>A strong seasonality in the dengue cases distribution was observed during epidemic years with outbreaks occurring usually between January and July. By contrast, dengue cases occurred almost every month without a clear seasonal pattern during non epidemic years.</p

SVM explicative model of dengue outbreaks in Noumea (leave-one-out cross validation).

<p>The model estimates the probability of dengue outbreak occurrence (red bars) each year according to the number of days with maximal temperature exceeding 32°C during the first quarter of the year (NOD_max Temp_32_JFM), and the number of days with maximal relative humidity exceeding 95% during January (NOD_max RH_95_January). Results obtained in leave-one-out cross validation are presented in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001470#pntd-0001470-g005" target="_blank">Figure 5a</a>. The black line indicates the annual dengue incidence rate, and black diamonds indicate epidemic years according to the median method. The ROC curve (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001470#pntd-0001470-g005" target="_blank">Figure 5b</a>) indicates the rates of true and false positives for different detection thresholds. For example, for a probability of dengue outbreak above 65% (0.65), 15 of 20 epidemic years are predicted correctly (true positive rate = 75%) with only one false alarm (false positive rate = 5%). The sensitivity of the model for this threshold is 75% (15 epidemic years predicted correctly/20 epidemic years), the specificity 95% (19 non epidemic years predicted correctly/20 non epidemic years), the positive predictive value 94% (15 epidemic years predicted correctly/16 epidemic years predicted by the model), and the negative predictive value 79% (19 non epidemic years predicted correctly/24 non epidemic years predicted by the model).</p