Spatial patterns associated with the distribution of immature stages of Aedes aegypti in three dengue high-risk municipalities of Southwestern Colombia.

Aedes aegypti mosquitoes are the main vector of human arbovirosis in tropical and subtropical areas. Their adaptation to urban and rural environments generates infestations inside households. Therefore, entomological surveillance associated with spatio-temporal analysis is an innovative approach for vector control and dengue management. Here, our main aim was to inspect immature pupal stages in households belonging to municipalities at high risk of dengue in Cauca, Colombia, by implementing entomological indices and relating how they influence adult mosquitos' density. We provide novel data for the geographical distribution of 3,806 immature pupal stages of Ae. aegypti. We also report entomological indices and spatial characterization. Our results suggest that, for Ae. aegypti species, pupal productivity generates high densities of adult mosquitos in neighbouring households, evidencing seasonal behaviour. Our dataset is essential as it provides an innovative strategy for mitigating vector-borne diseases using vector spatial patterns. It also delineates the association between these vector spatial patterns, entomological indicators, and breeding sites in high-risk neighbourhoods.

Daily dataset of precipitation and temperature in the Department of Cauca, Colombia.

This study used the geostatistical Kriging methodology to reduce the spatial scale of a host of daily meteorological variables in the Department of Cauca (Colombia), namely, total precipitation and maximum, minimum, and average temperature. The objective was to supply a high-resolution database from 01/01/2015 to 31/12/2021 in order to support the climate component in a project led by the National Institute of Health (INS) named "Spatial Stratification of dengue based on the identification of risk factors: a pilot study in the Department of Cauca". The scaling process was applied to available databases from satellite information and reanalysis sources, specifically, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station Data), ERA5-Land (European Centre for Medium-Range Weather Forecasts), and MSWX (Multi-Source Weather). The 0.1° resolution offered by both the MSWX and ERA5-Land databases and the 0.05° resolution found in CHIRPS, was successfully reduced to a scale of 0.01° across all variables. Statistical metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Person Correlation Coefficient (r), and Mean Bias Error (MBE) were used to select the database that best estimated each variable. As a result, it was determined that the scaled ERA5-Land database yielded the best performance for precipitation and minimum daily temperature. On the other hand, the scaled MSWX database showed the best behavior for the other two variables of maximum temperature and daily average temperature. Additionally, using the scaled meteorological databases improved the performance of the regression models implemented by the INS for constructing a dengue early warning system.

Current and Future Climate Extremes Over Latin America and Caribbean: Assessing Earth System Models from High Resolution Model Intercomparison Project (HighResMIP).

Extreme temperature and precipitation events are the primary triggers of hazards, such as heat waves, droughts, floods, and landslides, with localized impacts. In this sense, the finer grids of Earth System models (ESMs) could play an essential role in better estimating extreme climate events. The performance of High Resolution Model Intercomparison Project (HighResMIP) models is evaluated using the Expert Team on Climate Change Detection and Indices (ETCCDI) over the 1981-2014 period and future changes (2021-2050) under Shared Socioeconomic Pathway SSP5-8.5, over ten regions in Latin America and the Caribbean. The impact of increasing the horizontal resolution in estimating extreme climate variability on a regional scale is first compared against reference gridded datasets, including reanalysis, satellite, and merging products. We used three different groups based on the resolution of the model's grid (sg): (i) low (0.8° ≤ sg ≤ 1.87°), (ii) intermediate (0.5° ≤ sg ≤ 0.7°), and (iii) high (0.23° ≥ sg ≤ 0.35°). Our analysis indicates that there was no clear evidence to support the posit that increasing horizontal resolution improves model performance. The ECMWF-IFS family of models appears to be a plausible choice to represent climate extremes, followed by the ensemble mean of HighResMIP in their intermediate resolution. For future climate, the projections indicate a consensus of temperature and precipitation climate extremes increase across most of the ten regions. Despite the uncertainties presented in this study, climate models have been and will continue to be an important tool for assessing risk in the face of extreme events. Supplementary Information: The online version contains supplementary material available at 10.1007/s41748-022-00337-7.

Publishing data to support the fight against human vector-borne diseases.

Vector-borne diseases are responsible for more than 17% of human cases of infectious diseases. In most situations, effective control of debilitating and deadly vector-bone diseases (VBDs), such as malaria, dengue, chikungunya, yellow fever, Zika and Chagas requires up-to-date, robust and comprehensive information on the presence, diversity, ecology, bionomics and geographic spread of the organisms that carry and transmit the infectious agents. Huge gaps exist in the information related to these vectors, creating an essential need for campaigns to mobilise and share data. The publication of data papers is an effective tool for overcoming this challenge. These peer-reviewed articles provide scholarly credit for researchers whose vital work of assembling and publishing well-described, properly-formatted datasets often fails to receive appropriate recognition. To address this, GigaScience's sister journal GigaByte partnered with the Global Biodiversity Information Facility (GBIF) to publish a series of data papers, with support from the Special Programme for Research and Training in Tropical Diseases (TDR), hosted by the World Health Organisation (WHO). Here we outline the initial results of this targeted approach to sharing data and describe its importance for controlling VBDs and improving public health.

Arbovirus vectors in municipalities with a high risk of dengue in Cauca, Southwestern Colombia.

The Culicidae family has two of the most important disease vector genus: Aedes spp. and Culex spp. Both of these are involved in the transmission of arboviruses. Here, we provide novel data for the geographical distribution of 2,383 specimens in the Culicidae family. We also report the percentage of houses infested with these vectors, and Shannon and Simpson diversity indices in three municipalities located in Cauca, Colombia. This dataset is relevant for research on vector-borne diseases because Aedes spp. can transmit arboviruses such as dengue, Zika and chikungunya, and Culex spp. is a well-known vector of West Nile virus and Venezuelan equine encephalitis.

Missing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networks.

Changes observed in the current climate and projected for the future significantly concern researchers, decision-makers, and the general public. Climate indices of extreme rainfall events are a trend assessment tool to detect climate variability and change signals, which have an average reliability at least in the short term and given climatic inertia. This paper shows 12 climate indices of extreme rainfall events for annual and seasonal scales for 12 climate stations between 1969 to 2019 in the Metropolitan area of Cali (southwestern Colombia). The construction of the indices starts from daily rainfall time series, which although have between 0.5% and 5.4% of missing data, can affect the estimation of the indices. Here, we propose a methodology to complete missing data of the extreme event indices that model the peaks in the time series. This methodology uses an artificial neural network approach known as Non-Linear Principal Component Analysis (NLPCA). The approach reconstructs the time series by modulating the extreme values of the indices, a fundamental feature when evaluating extreme rainfall events in a region. The accuracy in the indices estimation shows values close to 1 in the Pearson's Correlation Coefficient and in the Bi-weighting Correlation. Moreover, values close to 0 in the percent bias and RMSE-observations standard deviation ratio. The database provided here is an essential input in future evaluation studies of extreme rainfall events in the Metropolitan area of Cali, the third most crucial urban conglomerate in Colombia with more than 3.9 million inhabitants.

Climatological aspects and changes in temperature and precipitation extremes in Viçosa-Minas Gerais.

Extreme climatic events and their hazards have strong impact on society. Urban areas in Brazil are especially vulnerable to the impact of such events due to their rapid growth and inappropriate infrastructure. Viçosa is a mid-sized city in Southeastern Brazil that has been experiencing issues associated with urban expansion and population growth since the 1960s. Thus, this study aims to identify patterns of extreme climate events in Viçosa based on daily temperature and precipitation time series (1968-2017). Homogeneity tests were carried out in order to identify breaking points in these climate variables. Climate trends were analyzed through Mann-Kendall test and their magnitude was checked based on Sen's slope. Results have evidenced statistically significant and increasing trends in annual minimum temperature since the 1990s. Moreover, statistically significant breaking points in extreme temperature indices have shown increasing number of warm days, and decreasing number of cold nights, in both annual and seasonal analyses. Extreme climatic events have been observed more often in recent years, mainly in the number of consecutive dry days and maximum and heavy precipitation days. Based on results, Viçosa experiences warmer conditions throughout the year, whereas more (less) torrential rainfall events have been occurring during Summer (Winter).

Análisis de aspectos que incrementan el riesgo de inundaciones en Colombia / Analyis of the aaspects which increase the risk of floods in Colombia

Este artículo tiene por objetivo analizar algunos aspectos que potenciaron las inundaciones en Colombia en el periodo 2010/2011. La metodología consistió en la revisión y síntesis de información sobre diferentes enfoques para el manejo de inundaciones, en armonía con la visión ecosistémica y de gestión integral de los recursos hídricos. Los resultados indican que las temporadas de lluvias y el fenómeno La Niña de abril de 2010 a diciembre de 2011 dejaron 5,2 millones de personas afectadas y 683 muertos. Dicho evento es el más crítico en la historia, ya que duplica las pérdidas acumuladas por inundaciones en 30 años. Además de lo anterior, se identifica que el desastre se debió a una suma de factores, entre los que destacan: cambios en la cobertura vegetal; las deficiencias en la gestión del suelo y los recursos hídricos; las debilidades en el diseño y gestión de infraestructura, en los patrones de crecimiento demográfico; los problemas institucionales en la prevención y atención de emergencias; la falta de transparencia y la corrupción; una legislación que carece de metodologías para aplicación, de recursos fiscales y de mecanismos para el seguimiento y medición resultados; entre otros. Una de las conclusiones es la necesidad de incluir nuevos conocimientos del clima en la gestión del riesgo y la planificación pero, sobre todo, es recomendable abordar el manejo de las inundaciones con un enfoque diferente, migrando hacia procesos integrales y con mayor capacidad de adaptación de la sociedad colombiana en los próximos años, mediante una gestión del riesgo de inundación más eficiente, acorde a conceptos modernos y en armonía con los procesos de desarrollo sostenible.

The main objective of this article is to analyze some aspects that fostered floods in Colombia in the period 2010/2011. The methodology followed was a review and synthesis of information on different approaches to flood management, in harmony with the ecosystemic vision and integrated management of water resources. The results indicate that the rainy season and La Niña phenomenon from April 2010 to December 2011 left 5,2 million people affected and 683 people dead. This has been the most critical event in history, because it doubled the accumulated losses from flooding in 30 years. Besides the above, it has been identified that the disaster was due to a combination of factors, among which are: changes in vegetation cover, deficiencies in the management of land and water resources, the weaknesses in the design and management of infrastructure, the population growth patterns, the institutional problems in the prevention and emergency care, lack of transparency and corruption, legislation that lacks enforcement methodologies, fiscal resources and mechanisms for monitoring and measuring the results, among others. One conclusion is the need to include new knowledge on climate, risk management and planning, but above all, it is recommended to address the management of floods with a different approach, migrating to more comprehensive processes and with a higer adaptation capacity from the Colombian society in the coming years through a more efficient flood risk management, in accordance with the modern concepts and in harmony with sustainable development processes.