Analysis of polymorphisms in the merozoite surface protein-3α gene and two microsatellite loci in Sri Lankan Plasmodium vivax: evidence of population substructure in Sri Lanka.

The geographical distribution of genetic variation in Plasmodium vivax samples (N = 386) from nine districts across Sri Lanka is described using three markers; the P. vivax merozoite surface protein-3α (Pvmsp-3α) gene, and the two microsatellites m1501 and m3502. At Pvmsp-3α, 11 alleles were found with an expected heterozygosity (H(e)) of 0.81, whereas at m1501 and m3502, 24 alleles (H(e) = 0.85) and 8 alleles (H(e) = 0.74) were detected, respectively. Overall, 95 unique three locus genotypes were detected among the 279 samples positive at all three loci (H(e) = 0.95). Calculating the pairwise fixation index (F(ST)) revealed statistically significant population structure. The presence of identical 2-loci microsatellite genotypes in a significant proportion of samples revealed local clusters of closely related isolates contributing to strong linkage disequilibrium between marker alleles. The results show evidence of high genetic diversity and possible population substructure of P. vivax populations in Sri Lanka

Temporal correlation between malaria and rainfall in Sri Lanka

<p>Abstract</p> <p>Background</p> <p>Rainfall data have potential use for malaria prediction. However, the relationship between rainfall and the number of malaria cases is indirect and complex.</p> <p>Methods</p> <p>The statistical relationships between monthly malaria case count data series and monthly mean rainfall series (extracted from interpolated station data) over the period 1972 – 2005 in districts in Sri Lanka was explored in four analyses: cross-correlation; cross-correlation with pre-whitening; inter-annual; and seasonal inter-annual regression.</p> <p>Results</p> <p>For most districts, strong positive correlations were found for malaria time series lagging zero to three months behind rainfall, and negative correlations were found for malaria time series lagging four to nine months behind rainfall. However, analysis with pre-whitening showed that most of these correlations were spurious. Only for a few districts, weak positive (at lags zero and one) or weak negative (at lags two to six) correlations were found in pre-whitened series. Inter-annual analysis showed strong negative correlations between malaria and rainfall for a group of districts in the centre-west of the country. Seasonal inter-annual analysis showed that the effect of rainfall on malaria varied according to the season and geography.</p> <p>Conclusion</p> <p>Seasonally varying effects of rainfall on malaria case counts may explain weak overall cross-correlations found in pre-whitened series, and should be taken into account in malaria predictive models making use of rainfall as a covariate.</p

how much more and how much better?

Funding Information: We thank two autonomous reviewers and the team of Climate Policy editors for their excellent comments and suggestions. We are thankful for the financial support of the German Federal Ministry of Economic Cooperation and Development (BMZ) for this research. The research was done independently, BMZ had no role in the preparation, analysis or writing of this article or its outcomes. Publisher Copyright: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.Formal deliberations for the new collective quantified goal on climate finance began at COP26 in Glasgow. This Perspectives article aims to inform this process by discussing the potential size and nature of is post-2025 target. We argue that the climate finance system around the current target to mobilise US$100 billion per year to support developing countries has been fraught with difficulties, and that it would be ineffective to simply increase the climate finance target without addressing these difficulties. Therefore, we identify and discuss five priority elements for negotiations: the relation to Article 2.1(c) of the Paris Agreement; the adaptation-mitigation balance; financial instruments; mobilising private finance; and ‘new and additional’ finance. To increase transparency, accountability, and trust in climate finance under the UNFCCC and to simultaneously allow for the mobilisation of finance at scale, we suggest setting a sub-target for grants. In combination with additional (sub)target(s), this could define an overall new collective quantified goal that is better suited to serve the challenging dual role of mobilising finance at scale and transferring resources to developing countries. Key policy insights: Ambiguous definitions of climate finance and the US$100 billion target allow for multiple interpretations, reducing transparency and trust between countries. Climate finance targets can be interpreted in a dual and sometimes contrasting way: mobilising investment at scale and transferring resources from developed to developing countries. Recognising this duality may help to find common ground for a post-2025 climate finance target. Increasing the climate finance target may prove ineffective without further clarity on private finance mobilisation, the relation to Art. 2.1(c), and other priority elements. More detailed assessments of needs, priorities, costs, and support are needed to inform the post-2025 target and assess climate finance provision effectiveness. A sub-target for grants could increase accountability, trust, and transparency, and target the needs of the most vulnerable developing countries. Negotiations on the post-2025 climate finance target could also consider additional aspects such as access to and prioritisation of finance, and loss and damage.publishersversionpublishe

Molecular evidence for the presence of malaria vector species a of the Anopheles annularis complex in Sri Lanka

<p>Abstract</p> <p>Background</p> <p><it>Anopheles annularis s.l</it>. is a wide spread malaria vector in South and Southeast Asia, including Sri Lanka. The taxon <it>An. annularis </it>is a complex of two sibling species viz. A and B, that are differentiated by chromosome banding patterns and ribosomal gene sequences in India. Only species A is reported to be a malaria vector in India while the occurrence of sibling species in Sri Lanka has not been documented previously.</p> <p>Findings</p> <p>Anopheline larvae were collected at a site in the Jaffna district, which lies within the dry zone of Sri Lanka, and reared in the laboratory. Emerged adults were identified using standard keys. DNA sequences of the D3 domain of 28S ribosomal DNA (rDNA) and the internal transcribed spacer-2 (ITS-2) of the morphologically identified <it>An. annularis </it>were determined. BLASTn searches against corresponding <it>An. annularis </it>sequences in GenBank and construction of phylogenetic trees from D3 and ITS-2 rDNA sequences showed that the Sri Lankan specimens, and <it>An. annularis s.l</it>. specimens from several Southeast Asian countries were closely related to species A of the Indian <it>An. annularis </it>complex.</p> <p>Conclusions</p> <p>The results show the presence of the malaria vector <it>An. annularis </it>species A in Sri Lanka and Southeast Asia. Because <it>An. annularis </it>vectors have been long associated with malaria transmission in irrigated agricultural areas in the Sri Lankan dry zone, continued monitoring of <it>An. annularis </it>populations, and their sibling species status, in these areas need to be integral to malaria control and eradication efforts in the island.</p

Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and OCT Imaging

Purpose: To develop and validate a deep learning (DL) system for predicting each point on visual fields (VFs) from disc and OCT imaging and derive a structure–function mapping. Design: Retrospective, cross-sectional database study. Participants: A total of 6437 patients undergoing routine care for glaucoma in 3 clinical sites in the United Kingdom. Methods: OCT and infrared reflectance (IR) optic disc imaging were paired with the closest VF within 7 days. EfficientNet B2 was used to train 2 single-modality DL models to predict each of the 52 sensitivity points on the 24-2 VF pattern. A policy DL model was designed and trained to fuse the 2 model predictions. Main Outcome Measures: Pointwise mean absolute error (PMAE). Results: A total of 5078 imaging scans to VF pairs were used as a held-out test set to measure the final performance. The improvement in PMAE with the policy model was 0.485 (0.438, 0.533) decibels (dB) compared with the IR image of the disc alone and 0.060 (0.047, 0.073) dB with to the OCT alone. The improvement with the policy fusion model was statistically significant (P < 0.0001). Occlusion masking shows that the DL models learned the correct structure–function mapping in a data-driven, feature agnostic fashion. Conclusions: The multimodal, policy DL model performed the best; it provided explainable maps of its confidence in fusing data from single modalities and provides a pathway for probing the structure–function relationship in glaucoma

Spatial heterogeneity of habitat suitability for Rift Valley fever occurrence in Tanzania: an ecological niche modelling approach

Despite the long history of Rift Valley fever (RVF) in Tanzania, extent of its suitable habitat in the country remains unclear. In this study we investigated potential effects of temperature, precipitation, elevation, soil type, livestock density, rainfall pattern, proximity to wild animals, protected areas and forest on the habitat suitability for RVF occurrence in Tanzania. Presence-only records of 193 RVF outbreak locations from 1930 to 2007 together with potential predictor variables were used to model and map the suitable habitats for RVF occurrence using ecological niche modelling. Ground-truthing of the model outputs was conducted by comparing the levels of RVF virus specific antibodies in cattle, sheep and goats sampled from locations in Tanzania that presented different predicted habitat suitability values. Habitat suitability values for RVF occurrence were higher in the northern and central-eastern regions of Tanzania than the rest of the regions in the country. Soil type and precipitation of the wettest quarter contributed equally to habitat suitability (32.4% each), followed by livestock density (25.9%) and rainfall pattern (9.3%). Ground-truthing of model outputs revealed that the odds of an animal being seropositive for RVFV when sampled from areas predicted to be most suitable for RVF occurrence were twice the odds of an animal sampled from areas least suitable for RVF occurrence (95% CI: 1.43, 2.76, p < 0.001). The regions in the northern and central-eastern Tanzania were more suitable for RVF occurrence than the rest of the regions in the country. The modelled suitable habitat is characterised by impermeable soils, moderate precipitation in the wettest quarter, high livestock density and a bimodal rainfall pattern. The findings of this study should provide guidance for the design of appropriate RVF surveillance, prevention and control strategies which target areas with these characteristics

Models for short term malaria prediction in Sri Lanka

<p>Abstract</p> <p>Background</p> <p>Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control.</p> <p>Methods</p> <p>Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models.</p> <p>Results</p> <p>The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons.</p> <p>Conclusion</p> <p>Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed.</p

Larval Development of Aedes aegypti and Aedes albopictus in Peri-Urban Brackish Water and Its Implications for Transmission of Arboviral Diseases

Aedes aegypti (Linnaeus) and Aedes albopictus Skuse mosquitoes transmit serious human arboviral diseases including yellow fever, dengue and chikungunya in many tropical and sub-tropical countries. Females of the two species have adapted to undergo preimaginal development in natural or artificial collections of freshwater near human habitations and feed on human blood. While there is an effective vaccine against yellow fever, the control of dengue and chikungunya is mainly dependent on reducing freshwater preimaginal development habitats of the two vectors. We show here that Ae. aegypti and Ae. albopictus lay eggs and their larvae survive to emerge as adults in brackish water (water with <0.5 ppt or parts per thousand, 0.5–30 ppt and >30 ppt salt are termed fresh, brackish and saline respectively). Brackish water with salinity of 2 to 15 ppt in discarded plastic and glass containers, abandoned fishing boats and unused wells in coastal peri-urban environment were found to contain Ae. aegypti and Ae. albopictus larvae. Relatively high incidence of dengue in Jaffna city, Sri Lanka was observed in the vicinity of brackish water habitats containing Ae. aegypti larvae. These observations raise the possibility that brackish water-adapted Ae. aegypti and Ae. albopictus may play a hitherto unrecognized role in transmitting dengue, chikungunya and yellow fever in coastal urban areas. National and international health authorities therefore need to take the findings into consideration and extend their vector control efforts, which are presently focused on urban freshwater habitats, to include brackish water larval development habitats