Alarm Variables for Dengue Outbreaks: A Multi-Centre Study in Asia and Latin America

Abstract

Background: Worldwide, dengue is an unrelenting economic and health burden. Dengue outbreaks have become increasingly common, which place great strain on health infrastructure and services. Early warning models could allow health systems and vector control programmes to respond more cost-effectively and efficiently.

Methodology/principal findings: The Shewhart method and Endemic Channel were used to identify alarm variables that may predict dengue outbreaks. Five country datasets were compiled by epidemiological week over the years 2007-2013. These data were split between the years 2007-2011 (historic period) and 2012-2013 (evaluation period). Associations between alarm/ outbreak variables were analysed using logistic regression during the historic period while alarm and outbreak signals were captured during the evaluation period. These signals were combined to form alarm/ outbreak periods, where 2 signals were equal to 1 period. Alarm periods were quantified and used to predict subsequent outbreak periods. Across Mexico and Dominican Republic, an increase in probable cases predicted outbreaks of hospitalised cases with sensitivities and positive predictive values (PPV) of 93%/ 83% and 97%/ 86% respectively, at a lag of 1-12 weeks. An increase in mean temperature ably predicted outbreaks of hospitalised cases in Mexico and Brazil, with sensitivities and PPVs of 79%/ 73% and 81%/ 46% respectively, also at a lag of 1-12 weeks. Mean age was predictive of hospitalised cases at sensitivities and PPVs of 72%/ 74% and 96%/ 45% in Mexico and Malaysia respectively, at a lag of 4-16 weeks.

Conclusions/significance: An increase in probable cases was predictive of outbreaks, while meteorological variables, particularly mean temperature, demonstrated predictive potential in some countries, but not all. While it is difficult to define uniform variables applicable in every country context, the use of probable cases and meteorological variables in tailored early warning systems could be used to highlight the occurrence of dengue outbreaks or indicate increased risk of dengue transmission.

Fig 1. Modelling with a test dataset (3 years only) using the z value (z = 1.25) to form the Endemic Channel.

Outbreak signals were detected (black dots) where incidence crossed the Endemic Channel. Outbreak periods (red dots) were formed when 2 consecutive outbreak signals were present; outbreak periods ended when 2 absent consecutive outbreak signals were registered (incidence did not cross the Endemic Channel for 2 consecutive weeks).

Fig 2

Modelling with a test dataset (3 years only) using two z values (Top: z = 1.25; Bottom: z = 2.0) to form the Endemic Channel. Outbreak periods (red dots) were equal to two consecutive outbreak signals (black dots) and ended in the absence of 2 consecutive outbreak signals.

Fig 3. Test dataset: Alarm threshold of 0.12 was used against the outbreak probability.

Alarm periods (defined by 2 alarm signals (black dots) within the lag period) successfully detected outbreak periods (red dots) (defined by 2 outbreak signals). Correct and false alarms are highlighted. z = 1.25.

Fig 4. Test dataset: Alarm threshold of 0.13 was used against the outbreak probability.

Alarm periods (defined by 2 alarm signals (black dots) within the lag period) successfully detected outbreak periods (red dots) (defined by 2 outbreak signals). Correct and false alarms are highlighted. z = 1.25.

Fig 5. Test dataset: Alarm threshold = 0.12, z-value = 1.25.

Top: alarm periods defined by 2 alarms signals (black dots). Bottom: alarm periods defined by 4 alarm signals.

Fig 6. Performance testing of the outbreak probability using 3 country datasets (evaluation period).

Sensitivity. z-value = 1.25, alternative alarm thresholds. Brazil: Alarm variable = Probable Cases; Outbreak variable = Hospitalised Cases; Mexico: Alarm variable = mean temperature; Outbreak variable = Hospitalised Cases; Malaysia: Alarm variable = Mean age; Outbreak variable = Hospitalised Cases.

Fig 7. Performance testing of the outbreak probability using 3 country datasets (evaluation period).

Positive Predictive Value. z-value = 1.25, alternative alarm thresholds. Brazil: Alarm variable = Probable Cases; Outbreak variable = Hospitalised Cases; Mexico: Alarm variable = Mean Temperature; Outbreak variable = Hospitalised Cases; Malaysia: Alarm variable = Mean Age; Outbreak variable = Hospitalised Cases.

Fig 8. Performance testing of the outbreak probability using 3 country datasets (evaluation period).

Sensitivity. Alarm threshold = 0.12, alternative z-values. Brazil: Alarm variable = Probable Cases; Outbreak variable = Hospitalised Cases; Mexico: Alarm variable = Mean Temperature; Outbreak variable = Hospitalised Cases; Malaysia: Alarm variable = Mean Age; Outbreak variable = Hospitalised Cases.

Fig 9. Performance testing of the outbreak probability using 3 country datasets (evaluation period).

Positive Predictive Value. Alarm threshold = 0.12, alternative z-values. Brazil: Alarm variable = Probable Cases; Outbreak variable = Hospitalised Cases; Mexico: Alarm variable = Mean Temperature; Outbreak variable = Hospitalised Cases; Malaysia: Alarm variable = Mean Age; Outbreak variable = Hospitalised Cases.