Predicting climate-sensitive water-related disease trends based on health, seasonality and weather data in Fiji

Leptospirosis, typhoid and dengue are three water-related diseases influenced by environmental factors. We examined whether seasonality and rainfall predict reported syndromes associated with leptospirosis, typhoid and dengue in Fiji. Poisson generalised linear models were fitted with s6 early warning, alert and response system (EWARS) syndromic conditions from March 2016 until December 2020, incorporating seasonality, temperature and rainfall. Watery diarrhoea, prolonged fever and suspected dengue displayed seasonal trends with peaks corresponding with the rainy season, while bloody diarrhoea, acute fever with rash and acute jaundice syndrome did not. Seasonality was the most common predictor for watery and bloody diarrhoea, prolonged fever, suspected dengue, and acute fever plus rash in those aged 5 and over, explaining between 0.4 % – 37.8 % of the variation across all conditions. Higher rainfall was the most common predictor for acute fever plus rash and acute jaundice syndrome in children under 5, explaining between 1.0 % – 7.6 % variation across all conditions. Each EWARS syndromic condition case peak was associated with a different rainfall lag, varying between 0 and 11 weeks. The relationships between EWARS, rainfall and seasonality show that it is possible to predict when outbreaks will occur by following seasonality and rainfall. Pre-positioning of diagnostic and treatment resources could then be aligned with seasonality and rainfall peaks to plan and address water-related disease outbreaks

Successful introgression of wMel Wolbachia into Aedes aegypti populations in Fiji, Vanuatu and Kiribati.

Pacific Island countries have experienced periodic dengue, chikungunya and Zika outbreaks for decades. The prevention and control of these mosquito-borne diseases rely heavily on control of Aedes aegypti mosquitoes, which in most settings are the primary vector. Introgression of the intracellular bacterium Wolbachia pipientis (wMel strain) into Ae. aegypti populations reduces their vector competence and consequently lowers dengue incidence in the human population. Here we describe successful area-wide deployments of wMel-infected Ae. aegypti in Suva, Lautoka, Nadi (Fiji), Port Vila (Vanuatu) and South Tarawa (Kiribati). With community support, weekly releases of wMel-infected Ae. aegypti mosquitoes for between 2 to 5 months resulted in wMel introgression in nearly all locations. Long term monitoring confirmed a high, self-sustaining prevalence of wMel infecting mosquitoes in almost all deployment areas. Measurement of public health outcomes were disrupted by the Covid19 pandemic but are expected to emerge in the coming years

<i>w</i>Mel introgression in two areas in South Tarawa, Kiribati.

A) South Tarawa, Kiribati showing the two release areas: Betio (left) and Bairiki (right). B) Introgression of wMel. The line (left axis) represents the percent of Ae. aegypti tested that were infected with wMel Wolbachia, between May 2018 and December 2019. The bars (right axis) indicate the number of Ae. aegypti tested. Data points with less than five screened mosquitos have been omitted. Shaded orange areas indicate wMel mosquito release times. Map produced in QGIS version 3.16.1 using the enumeration area boundaries freely available from the Pacific Data Hub (https://pacificdata.org/data/dataset/2010_kir_phc_admin_boundaries) and OpenMapTiles basemap layer (https://openmaptiles.org/) with CARTO light design (https://carto.com/).</p

Release & monitoring of <i>w</i>Mel-infected <i>Ae</i>. <i>aegypti</i> within six areas of Nadi and five areas of Lautoka, Fiji.

Each release area was divided into a grid with 100 x 100 meter squares. Grid squares lacking mosquito releases were omitted. Release gradient was determined by using GPS coordinates of each release event and assigning the number of wMel-infected mosquitos to a corresponding grid square. Monitoring numbers were determined in the same way. Map produced in QGIS version 3.16.1 using boundaries aggregated from the enumeration area boundaries freely available from the Pacific Data Hub (https://pacificdata.org/data/dataset/2007_fji_phc_admin_boundaries) and OpenMapTiles basemap layer (https://openmaptiles.org/) with CARTO light design (https://carto.com/)). (PNG)</p

<i>w</i>Mel introgression in 12 release areas in Port Vila, Vanuatu.

A) Port Vila, Vanuatu showing the 12 release areas. B) wMel introgression. The line (left axis) represents the percent of Ae. aegypti screened that were infected with wMel Wolbachia, between August 2018 and May 2021. The bars (right axis) indicate the number of Ae. aegypti tested. Data points with less than five screened mosquitos have been omitted. Shaded orange areas indicate wMel mosquito release times. Map produced in QGIS version 3.16.1 using boundaries aggregated from the enumeration area boundaries freely available from the Pacific Data Hub (https://pacificdata.org/data/dataset/2016_vut_phc_admin_boundaries) and OpenMapTiles basemap layer (https://openmaptiles.org/) with CARTO light design (https://carto.com/)).</p

Suspected dengue cases notified in Port Vila from January 2016 –January 2022 by (A) hospitalisation status and (B) diagnostic test result.

Blue shading indicates release period for Wolbachia (wMel)-infected Ae. aegypti. Suspected dengue cases without any laboratory diagnostic testing are included in panel A, but excluded from panel B.</p

<i>w</i>Mel introgression in six release areas in Nadi, Fiji.

A) Nadi, Fiji showing the six release zones. B) wMel introgression. The line (left axis) represents the percent of Ae. aegypti tested that were infected with wMel Wolbachia, between July 2019 and January 2022. The bars (right axis) indicate the number of Ae. aegypti tested. Data points with less than five tested mosquitos have been omitted. Shaded orange areas indicate wMel mosquito release times. Map produced in QGIS version 3.16.1 using boundaries aggregated from the enumeration area boundaries freely available from the Pacific Data Hub (https://pacificdata.org/data/dataset/2007_fji_phc_admin_boundaries) and OpenMapTiles basemap layer (https://openmaptiles.org/) with CARTO light design (https://carto.com/)).</p

Suspected dengue cases notified in Kiribati from January 2009 –August 2022 by diagnostic test result.

Blue shading indicates release period for Wolbachia (wMel)-infected Ae. aegypti.</p

Release & monitoring of <i>w</i>Mel-infected <i>Ae</i>. <i>aegypti</i> within 12 areas of Suva and Lami, Fiji.

Each release area was divided into a grid with 100 x 100 meter squares. Grid squares lacking mosquito releases were omitted. Release gradient was determined by using GPS coordinates of each release event and assigning the number of wMel-infected mosquitos to a corresponding grid square. Monitoring numbers were determined in the same way. Map produced in QGIS version 3.16.1 using boundaries aggregated from the enumeration area boundaries freely available from the Pacific Data Hub (https://pacificdata.org/data/dataset/2007_fji_phc_admin_boundaries) and OpenMapTiles basemap layer (https://openmaptiles.org/) with CARTO light design (https://carto.com/)). (PNG)</p

Suspected chikungunya cases notified in Kiribati from January 2017 –August 2022.

Blue shading indicates release period for Wolbachia (wMel)-infected Ae. aegypti.</p