Climate change and its effect on the vulnerability to zoonotic cutaneous leishmaniasis in Iran

Zoonotic cutaneous leishmaniasis (ZCL) is an important vector-borne disease with an incidence of 15.8 cases per 100,000 people in Iran in 2019. Despite all efforts to control the disease, ZCL has expanded into new areas during the last decades. The aim of this study was to predict the best ecological niches for both vectors and reservoirs of ZCL under climate change scenarios in Iran. Several online scientific databases were searched. In this study, various scientific sources (Google Scholar, PubMed, SID, Ovid Medline, Web of Science, Irandoc, Magiran) were searched. The inclusion criteria for this study included all records with spatial information about vectors and reservoirs of ZCL which were published between 1980 and 2019. The bioclimatic data were downloaded from online databases. MaxEnt model was used to predict the ecological niches for each species under two climate change scenarios in two periods: the 2030s and 2050s. The results obtained from the model were analysed in ArcMap to find the vulnerability of different provinces for the establishment of ZCL foci. The area under the curve (AUC) for all models was >0.8, which suggests the models are able to make an accurate prediction. The distribution of all studied species in different climatic conditions showed changes. The variables affecting each of the studied species are introduced in the article. The predicted maps show that by 2050 there will be more suitable areas for the co-occurrence of vector and reservoir(s) of ZCL in Iran compared to the current climate condition and RCP2.6 scenario. An area in the northwest of Iran is predicted to have suitable environmental conditions for both vectors and reservoirs of ZCL, although the disease has not yet been reported in this area. These areas should be considered for field studies to confirm these results and to prevent the establishment of new ZCL foci in Iran.

Modelling of malaria risk, rates, and trends: A spatiotemporal approach for identifying and targeting sub-national areas of high and low burden

While mortality from malaria continues to decline globally, incidence rates in many countries are rising. Within countries, spatial and temporal patterns of malaria vary across communities due to many different physical and social environmental factors. To identify those areas most suitable for malaria elimination or targeted control interventions, we used Bayesian models to estimate the spatiotemporal variation of malaria risk, rates, and trends to determine areas of high or low malaria burden compared to their geographical neighbours. We present a methodology using Bayesian hierarchical models with a Markov Chain Monte Carlo (MCMC) based inference to fit a generalised linear mixed model with a conditional autoregressive structure. We modelled clusters of similar spatiotemporal trends in malaria risk, using trend functions with constrained shapes and visualised high and low burden districts using a multi-criterion index derived by combining spatiotemporal risk, rates and trends of districts in Zambia. Our results indicate that over 3 million people in Zambia live in high-burden districts with either high mortality burden or high incidence burden coupled with an increasing trend over 16 years (2000 to 2015) for all age, under-five and over-five cohorts. Approximately 1.6 million people live in high-incidence burden areas alone. Using our method, we have developed a platform that can enable malaria programs in countries like Zambia to target those high-burden areas with intensive control measures while at the same time pursue malaria elimination efforts in all other areas. Our method enhances conventional approaches and measures to identify those districts which had higher rates and increasing trends and risk. This study provides a method and a means that can help policy makers evaluate intervention impact over time and adopt appropriate geographically targeted strategies that address the issues of both high-burden areas, through intensive control approaches, and low-burden areas, via specific elimination programs.

Correction: Doum, D., et al. Dengue Seroprevalence and Seroconversion in Urban and Rural Populations in Northeastern Thailand and Southern Laos. Int. J. Environ. Res. Public Health 2020, 17, 9134

There was an error in the original article [...].

Knowledge, attitudes, and practices on climate change and dengue in Lao People's Democratic Republic and Thailand

BACKGROUND

Dengue is linked with climate change in tropical and sub-tropical countries including the Lao People's Democratic Republic (Laos) and Thailand. Knowledge about these issues and preventive measures can affect the incidence and outbreak risk of dengue. Therefore, the present study was conducted to determine the knowledge, attitudes, and practices (KAP) among urban and rural communities and government officials about climate change and dengue in Laos and Thailand.

METHODS

A cross-sectional KAP survey about climate change and dengue were conducted in 360 households in Laos (180 urban and 180 rural), 359 households in Thailand (179 urban and 180 rural), and 20 government officials (10 in each country) using structured questionnaires. Data analysis was undertaken using descriptive methods, principal component analysis (PCA), Chi-square test or Fisher's exact test (as appropriate), and logistic regression.

RESULTS

Significant differences among the selected communities in both countries were found in terms of household participant's age, level of education, socioeconomic status, attitude level of climate change and KAP level of dengue (P < 0.05; 95% CI). Overall, participants' KAP about climate change and dengue were low except the attitude level for dengue in both countries. The level of awareness among government officials regarding the climatic relationship with dengue was also low. In Lao households, participants' knowledge about climate change and dengue was significantly associated with the level of education and socioeconomic status (SES) (P < 0.01). Their attitudes towards climate change and dengue were associated with educational level and internet use (P < 0.05). Householders' climate change related practices were associated with SES (P < 0.01) and dengue related practices were associated with educational level, SES, previous dengue experience and internet use (P < 0.01). In Thailand, participants' knowledge about climate change was associated with the level of education and SES (P < 0.01). Their attitudes towards climate change were associated with residence status (urban/rural) and internet use (P < 0.05); climate change related practices were associated with educational level and SES (P < 0.05). Dengue related knowledge of participants was associated with SES and previous dengue experience (P < 0.05); participants' dengue related attitudes and practices were associated with educational level (P < 0.01).

CONCLUSION

The findings call for urgently needed integrated awareness programs to increase KAP levels regarding climate change adaptation, mitigation and dengue prevention to improve the health and welfare of people in these two countries, and similar dengue-endemic countries.

A Computational Modeling Study of COVID-19 in Bangladesh

The COVID-19 pandemic has spread globally. Only three cases in Bangladesh were reported on March 8, 2020. Here, we aim to predict the epidemic progression for 1 year under different scenarios in Bangladesh. We extracted the number of daily confirmed cases from March 8 to July 20, 2020. We considered the suspected-infected-removed (SIR) model and performed a maximum likelihood-based grid search to determine the removal rate (ɣ). The transmission was modeled as a stochastic random walk process, and sequential Monte Carlo simulation was run 100 times with bootstrap fits to infer the transmission rate (β) and R _t. According to the simulation, the (real) peak daily incidence of 3,600 would be followed by a steady decline, reaching below 1,000 in late January 2021. Thus, the model predicted that there would still be more than 300 cases/day even after a year. However, with proper interventions, a much steeper decline would be achieved following the peak. If we apply a combined (0.8β, 1.2ɣ) intervention, there would be less than 100 cases by mid-October, only around five odd cases at the beginning of the year 2021, and zero cases in early March 2021. The predicted total number of deaths (in status quo) after 1 year would be 8,533 which would reduce to 3,577 if combined (0.8β, 1.2ɣ) intervention is applied. We have also predicted the ideal number of tests that Bangladesh should perform and based on that redid the whole simulation. The outcome, though worse, would be manageable with interventions according to the simulation.

Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques

Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980's, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.

COVID-19 in China: Risk Factors and R0 Revisited

The COVID-19 epidemic spread rapidly through China and subsequently proliferated globally leading to a pandemic situation around the globe. Human-to-human transmission, as well as asymptomatic transmission of the infection, have been confirmed. As of April 03, 2020, public health crisis in China due to COVID-19 was potentially under control. We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020. Understanding the characteristics of spatial clustering of the COVID-19 epidemic and R₀ is critical in effectively preventing and controlling the ongoing global pandemic. Considering this, the prefectures were grouped based on several relevant features using unsupervised machine learning techniques. Subsequently, we performed a computational analysis utilizing the reported cases in China to estimate the revised R₀ among different regions. Finally, our overall research indicates that the impact of temperature and demographic factors on virus transmission may be characterized using a stochastic transmission model. Such predictions will help in prevention planning in an ongoing global pandemic, prioritizing segments of a given community/region for action and providing a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.

COVID-19 Epidemic in Bangladesh among Rural and Urban Residents: An Online Cross-Sectional Survey of Knowledge, Attitudes, and Practices

As other nations around the world, Bangladesh is facing enormous challenges with the novel coronavirus (COVID-19) epidemic. To design a prevention and control strategy for this new infectious disease, it is essential to first understand people's knowledge, attitudes, and practices (KAP) regarding COVID-19. This study sought to determine KAP among rural and urban residents as well as predictors of preventive practices associated with COVID-19 in Bangladesh. A social media-based (Facebook) cross-sectional survey was conducted to explore these variables among Bangladeshi adults. Of 1520 respondents who completed the questionnaire, low level of good or sufficient knowledge of COVID-19 (70.8%) and practices associated with COVID-19 (73.8%) were found. Despite the low level of knowledge and practices, respondents' attitude (78.9%) towards COVID-19 was relatively high. Results suggest that compared to urban, rural residents are at a particularly high risk of COVID-19 because they were found to have significantly lower knowledge (p = 0.001) and practice levels (p = 0.002) than were urban residents. Multivariable logistic regression analysis identified gender, education, knowledge of COVID-19 transmission, signs and symptoms, and sources of information as factors significantly associated with preventive practices against COVID-19. Further attention and effort should be directed toward increasing both knowledge and practices targeting the general population in Bangladesh, particularly the rural and less educated residents. Findings from this study provide baseline data that can be used to promote integrated awareness of and effective health education programs about COVID-19 prevention and control strategies in Bangladesh, and similar COVID-19 endemic countries.

The impact of COVID-19 on globalization

Globalization has altered the way we live and earn a livelihood. Consequently, trade and travel have been recognized as significant determinants of the spread of disease. Additionally, the rise in urbanization and the closer integration of the world economy have facilitated global interconnectedness. Therefore, globalization has emerged as an essential mechanism of disease transmission. This paper aims to examine the potential impact of COVID-19 on globalization and global health in terms of mobility, trade, travel, and countries most impacted.

The effect of globalization were operationalized in terms of mobility, economy, and healthcare systems. The mobility of individuals and its magnitude was assessed using airline and seaport trade data and travel information. The economic impact was measured based on the workforce, event cancellations, food and agriculture, academic institutions, and supply chain. The healthcare capacity was assessed by considering healthcare system indicators and preparedness of countries. Utilizing a technique for order of preference by similarity to ideal solution (TOPSIS), we calculated a pandemic vulnerability index (PVI) by creating a quantitative measure of the potential global health. The pandemic has placed an unprecedented burden on the world economy, healthcare, and globalization through travel, events cancellation, employment workforce, food chain, academia, and healthcare capacity. Based on PVI results, certain countries were more vulnerable than others. In Africa, more vulnerable countries included South Africa and Egypt; in Europe, they were Russia, Germany, and Italy; in Asia and Oceania, they were India, Iran, Pakistan, Saudi Arabia, and Turkey; and for the Americas, they were Brazil, USA, Chile, Mexico, and Peru. The impact on mobility, economy, and healthcare systems has only started to manifest. The findings of this study may help in the planning and implementation of strategies at the country level to help ease this emerging burden.

The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned?

Objectives

To provide an overview of the three major deadly coronaviruses and identify areas for improvement of future preparedness plans, as well as provide a critical assessment of the risk factors and actionable items for stopping their spread, utilizing lessons learned from the first two deadly coronavirus outbreaks, as well as initial reports from the current novel coronavirus (COVID-19) epidemic in Wuhan, China.

Methods

Utilizing the Centers for Disease Control and Prevention (CDC, USA) website, and a comprehensive review of PubMed literature, we obtained information regarding clinical signs and symptoms, treatment and diagnosis, transmission methods, protection methods and risk factors for Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS) and COVID-19. Comparisons between the viruses were made.

Results

Inadequate risk assessment regarding the urgency of the situation, and limited reporting on the virus within China has, in part, led to the rapid spread of COVID-19 throughout mainland China and into proximal and distant countries. Compared with SARS and MERS, COVID-19 has spread more rapidly, due in part to increased globalization and the focus of the epidemic. Wuhan, China is a large hub connecting the North, South, East and West of China via railways and a major international airport. The availability of connecting flights, the timing of the outbreak during the Chinese (Lunar) New Year, and the massive rail transit hub located in Wuhan has enabled the virus to perforate throughout China, and eventually, globally.

Conclusions

We conclude that we did not learn from the two prior epidemics of coronavirus and were ill-prepared to deal with the challenges the COVID-19 epidemic has posed. Future research should attempt to address the uses and implications of internet of things (IoT) technologies for mapping the spread of infection.

A magnetic immunoconjugate nanoplatform for easy colorimetric detection of the NS1 protein of dengue virus in infected serum

In this work, as a proof of principle, the design and performance evaluation of a simple, cheap and efficient colorimetric test for the detection of the NS1 protein of dengue virus, assisted by an immunoconjugate of magnetite (Fe₃O₄) nanoparticles coupled to anti-NS1 antibodies is reported. A monoclonal antibody against the NS1 antigen was covalently immobilized on the surface of superparamagnetic iron oxide nanoparticles (SPIONs ∼ 20 nm) and used for the immunodetection of this protein. When the magnetic immuno-nanoplatform is added into infected serum, it conjugates with the NS1 protein and can then be easily separated using an external magnetic field; then, the recovered immunoconjugate is transferred into a well containing a second immobilized NS1-antibody to form an ELISA-type system. When the NS1 protein is present, a color change to blue is induced by reaction with the Perls reagent, which is consistent with the formation of a SPION-antibody-NS1 antigen-antibody conjugate that confirms infection. No false positives were found when NS1 was not present or a different antibody and the NS1 protein were added into the system. The experimental findings could be extrapolated and scaled up to lead to future developments of simple, quick, and inexpensive, in situ biomolecular diagnostic tests for emergent viral infections.

The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned?

OBJECTIVES

To provide an overview of the three major deadly coronaviruses and identify areas for improvement of future preparedness plans, as well as provide a critical assessment of the risk factors and actionable items for stopping their spread, utilizing lessons learned from the first two deadly coronavirus outbreaks, as well as initial reports from the current novel coronavirus (COVID-19) epidemic in Wuhan, China.

METHODS

Utilizing the Centers for Disease Control and Prevention (CDC, USA) website, and a comprehensive review of PubMed literature, we obtained information regarding clinical signs and symptoms, treatment and diagnosis, transmission methods, protection methods and risk factors for Middle East respiratory syndrome (MERS), severe acute respiratory syndrome (SARS) and COVID-19. Comparisons between the viruses were made.

RESULTS

Inadequate risk assessment regarding the urgency of the situation, and limited reporting on the virus within China has, in part, led to the rapid spread of COVID-19 throughout mainland China and into proximal and distant countries. Compared with SARS and MERS, COVID-19 has spread more rapidly, due in part to increased globalization and the focus of the epidemic. Wuhan, China is a large hub connecting the North, South, East and West of China via railways and a major international airport. The availability of connecting flights, the timing of the outbreak during the Chinese (Lunar) New Year, and the massive rail transit hub located in Wuhan has enabled the virus to perforate throughout China, and eventually, globally.

CONCLUSIONS

We conclude that we did not learn from the two prior epidemics of coronavirus and were ill-prepared to deal with the challenges the COVID-19 epidemic has posed. Future research should attempt to address the uses and implications of internet of things (IoT) technologies for mapping the spread of infection.

Defending against the Novel Coronavirus (COVID-19) outbreak: How can the Internet of Things (IoT) help to save the world?

•IoT within infectious disease epidemiology is an emerging field of research, however the ubiquitous availability of smart technologies, as well as increased risks of infectious disease spread through the globalization and interconnectedness of the world necessitates its use for predicting, preventing and controlling emerging infectious diseases.•Considering the present situation in China, IoT based smart disease surveillance systems have the potential to be a major breakthrough in efforts to control the current pandemic. With much of the infrastructure itself in place already (i.e. smartphones, wearable technologies, internet access) the role this technology can have in limiting the spread of the pandemic involves only the collection and analysis of data already gathered.•More research must be carried out for the development of automated and effective alert systems to provide early and timely detection of outbreaks of such diseases in order to reduce morbidity mortality and prevent global spread.

Malaria epidemics in India: Role of climatic condition and control measures

Malaria is a major public health problem in India, which is the second most populous country in the world. This study aimed to investigate the impact of climatic parameters and malaria control efforts implemented by the Indian national malaria control program on malaria epidemics between January of 2009 and December of 2015. A chi-squared test was used to study the correlation of all implemented control methods with occurrence of epidemics within 30, 45, 60 and 90 days and in the same district, 50, 100 and 200 km distance radiuses. The effect of each control method on probability of epidemics was also measured, and the effects of district population, season, and incidence of malaria parasite types were evaluated using logistic regression models. Fever survey was found to be effective for decreasing the odds of epidemics within 45, 60 and 90 days in 100 km. Anti-larval activity was also effective within 30, 45 and 60 days in 200 km. Winter had negative effects on odds ratio while summer and fall were more likely to trigger epidemics. These results contribute to understanding the role of climate variability and control efforts performed in India.

Retrospective data analyses of social and environmental determinants of malaria control for elimination prospects in Eritrea

Background

The present study focuses on both long- and short-term malaria transmission in Eritrea and investigates the risk factors. Annual aggregates of information on malaria cases, deaths, diagnostics and control interventions from 2001 to 2008 and monthly reported data from 2009 to 2017 were obtained from the National Malaria Control Programme. We used a generalized linear regression model to examine the associations among total malaria cases, death, insecticide-treated net coverage, indoor residual spraying and climatic parameters.

Results

Reduction in malaria mortality is demonstrated by the milestone margins of over 97% by the end of 2017. Malaria incidence likewise declined during the period (from 33 to 5 per 1000 population), representing a reduction of about 86% (R² = 0.3) slightly less than the decline in mortality. The distribution of insecticide treated nets generally declined between 2001 and 2014 (R² = 0.16) before increasing from 2015 to 2017, while the number of people protected by indoor residual spraying slightly increased (R² = 0.27). Higher rainfall was significantly associated with an increased number of malaria cases. The covariates rainfall and temperature are a better pair than IRS and LLIN to predict incidences. On the other hand, IRS and LLIN is a more significant pair to predict mortality cases.

Conclusions

While Eritrea has made significant progress towards malaria elimination, this progress should be maintained and further improved. Distribution, coverage and utilization of malaria control and elimination tools should be optimized and sustained to safeguard the gains made. Additionally, consistent annual performance evaluation of malaria indicators would ensure a continuous learning process from gains/threats of epidemics and resurgence in regions already earmarked for elimination.

An investigation of the Plasmodium falciparum malaria epidemic in Kavango and Zambezi regions of Namibia in 2016

Background

Namibia is one of the countries among the eight that are targeting malaria elimination in southern Africa. However, the country has encountered malaria epidemics in recent years. The objective of this study was to investigate malaria epidemics and to contribute to strengthening malaria surveillance and control in an effort to move Namibia toward eliminating malaria.

Method

Malaria epidemiology data for 2014-2015 were collected from the weekly surveillance system. All consenting household members within a 100-m radius of index households were screened in 2016 using a Carestart malaria HRP2/pLDH combined rapid diagnostic test after epidemics. All houses within this radius were sprayed in 2016 with the pyrethroid deltamethrin and K-Othrine WG 250. Anopheles mosquito-positive breeding sites were identified and treated with the organophosphate larvicide temephos. Insecticide susceptibility and bioassay tests were conducted.

Results

During the epidemic response period in 2016, 56 parasitologically confirmed Plasmodium falciparum malaria cases in the Zambezi region were detected from active screening. The majority of those cases (83%) were asymptomatic infections. In the Kavango region, the malaria epidemic persisted, with 228 P. falciparum malaria cases recorded, but only 97 were investigated. In Namibia, malaria vector susceptibility was detected to 4% dichlorodiphenyltrichloroethane. Indoor residual spraying was conducted in 377 (90%) of the targeted households along with community awareness through health education of 1499 people and distribution of more than 2000 information, education and communication materials. The P. falciparum malaria cases in the Zambezi decreased from 122 in week 9 to 97 after week 15.

Conclusions

Malaria epidemics along with the persistence of asymptomatic reservoir infections pose a serious challenge in Namibia's elimination effort. The country needs to ensure sustainable interventions to target asymptomatic reservoir infections and prevent epidemics in order to successfully achieve its goal of eliminating malaria.

The human cost of global warming: Deadly landslides and their triggers (1995-2014)

Worldwide, landslides incur catastrophic and significant economic and human losses. Previous studies have characterized the patterns in landslides' fatalities, from all kinds of triggering causes, at a continental or global scale, but they were based on data from periods of <10 years. The research herein presented hypothesizes that climate change associated with extreme rainfall and population distribution is contributing to a higher number of deadly landslides worldwide. This study maps and identified deadly landslides in 128 countries and it encompasses their role, for a 20 years' period from January/1995 to December/2014, considered representative for establishing a relationship between landslides and their meteorological triggers. A database of georeferenced landslides, their date, and casualties' information, duly validated, was implemented. A hot spot analysis for the daily record of landslide locations was performed, as well as a percentile-based approach to evaluate the trend of extreme rainfall events for each occurrence. The relationship between casualty, population distribution, and rainfall was also evaluated. For 20 years, 3876 landslides caused a total of 163,658 deaths and 11,689 injuries globally. They occurred most frequently between June and December in the Northern Hemisphere, and between December and February in the Southern Hemisphere. A significant global rise in the number of deadly landslides and hotspots across the studied period was observed. Analysis of daily rainfall confirmed that more than half of the events were in areas exposed to the risk of extreme rainfall. The relationships established between extreme rainfall, population distribution, seasonality, and landslides provide a useful basis for efforts to model the adverse impacts of extreme rainfall due to climate change and human activities and thus contribute towards a more resilient society.

Environmental suitability for Aedes aegypti and Aedes albopictus and the spatial distribution of major arboviral infections in Mexico

Background

This paper discusses a comparative geographic distribution of Aedes aegypti and Aedes albopictus mosquitoes in Mexico, using environmental suitability modeling and reported cases of arboviral infections.

Methods

Using presence-only records, we modeled mosquito niches to show how much they influenced the distribution of Ae. aegypti and Ae. albopictus based on mosquito records collected at the municipality level. Mosquito surveillance data were used to create models regarding the predicted suitability of Ae. albopictus and Ae. aegypti mosquitos in Mexico.

Results

Ae. albopictus had relatively a better predictive performance (area under the curve, AUC = 0.87) to selected bioclimatic variables compared to Ae. aegypti (AUC = 0.81). Ae. aegypti were more suitable for areas with minimum temperature of coldest month (Bio6, permutation importance 28.7%) −6 °C to 21.5 °C, cumulative winter growing degree days (GDD) between 40 and 500, and precipitation of wettest month (Bio13) >8.4 mm. Minimum temperature range of the coldest month (Bio6) was −6.6 °C to 20.5 °C, and average precipitation of the wettest month (Bio13) 8.9 mm ~ 600 mm were more suitable for the existence of Ae. albopictus. However, arboviral infections maps prepared from the 2012–2016 surveillance data showed cases were reported far beyond predicted municipalities.

Conclusions

This study identified the urgent necessity to start surveillance in 925 additional municipalities that reported arbovirus infections but did not report Aedes mosquito.

Threats of Zika virus transmission for Asia and its Hindu-Kush Himalayan region

Asia and its Hindu Kush Himalayan (HKH) region is particularly vulnerable to environmental change, especially climate and land use changes further influenced by rapid population growth, high level of poverty and unsustainable development. Asia has been a hotspot of dengue fever and chikungunya mainly due to its dense human population, unplanned urbanization and poverty. In an urban cycle, dengue virus (DENV) and chikungunya virus (CHIKV) are transmitted by Aedes aegypti and Ae. albopictus mosquitoes which are also competent vectors of Zika virus (ZIKV). Over the last decade, DENV and CHIKV transmissions by Ae. aegypti have extended to the Himalayan countries of Bhutan and Nepal and ZIKV could follow in the footsteps of these viruses in the HKH region. The already established distribution of human-biting Aedes mosquito vectors and a naïve population with lack of immunity against ZIKV places the HKH region at a higher risk of ZIKV. Some of the countries in the HKH region have already reported ZIKV cases. We have documented an increasing threat of ZIKV in Asia and its HKH region because of the high abundance and wide distribution of human-biting mosquito vectors, climate change, poverty, report of indigenous cases in the region, increasing numbers of imported cases and a naïve population with lack of immunity against ZIKV. An outbreak anywhere is potentially a threat everywhere. Therefore, in order to ensure international health security, all efforts to prevent, detect, and respond to ZIKV ought to be intensified now in Asia and its HKH region. To prepare for possible ZIKV outbreaks, Asia and the HKH region can also learn from the success stories and strategies adopted by other regions and countries in preventing ZIKV and associated complications. The future control strategies for DENV, CHIKV and ZIKV should be considered in tandem with the threat to human well-being that is posed by other emerging and re-emerging vector-borne and zoonotic diseases, and by the continuing urgent need to strengthen public primary healthcare systems in the region.

Modeling the Environmental Suitability for Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus (Diptera: Culicidae) in the Contiguous United States

The mosquitoes Aedes (Stegomyia) aegypti (L.)(Diptera:Culicidae) and Ae. (Stegomyia) albopictus (Skuse) (Diptera:Culicidae) transmit dengue, chikungunya, and Zika viruses and represent a growing public health threat in parts of the United States where they are established. To complement existing mosquito presence records based on discontinuous, non-systematic surveillance efforts, we developed county-scale environmental suitability maps for both species using maximum entropy modeling to fit climatic variables to county presence records from 1960-2016 in the contiguous United States. The predictive models for Ae. aegypti and Ae. albopictus had an overall accuracy of 0.84 and 0.85, respectively. Cumulative growing degree days (GDDs) during the winter months, an indicator of overall warmth, was the most important predictive variable for both species and was positively associated with environmental suitability. The number (percentage) of counties classified as environmentally suitable, based on models with 90 or 99% sensitivity, ranged from 1,443 (46%) to 2,209 (71%) for Ae. aegypti and from 1,726 (55%) to 2,329 (75%) for Ae. albopictus. Increasing model sensitivity results in more counties classified as suitable, at least for summer survival, from which there are no mosquito records. We anticipate that Ae. aegypti and Ae. albopictus will be found more commonly in counties classified as suitable based on the lower 90% sensitivity threshold compared with the higher 99% threshold. Counties predicted suitable with 90% sensitivity should therefore be a top priority for expanded mosquito surveillance efforts while still keeping in mind that Ae. aegypti and Ae. albopictus may be introduced, via accidental transport of eggs or immatures, and potentially proliferate during the warmest part of the year anywhere within the geographic areas delineated by the 99% sensitivity model.

Spatial distribution of Zika virus infection in Northeastern Colombia

In this study, we investigated the weekly reported spatio-temporal distribution and topographic risk factors for Zika virus (ZIKV) infection in northeastern Colombia. Weekly reported surveillance data, including clinical, suspected and confirmed cases from the ongoing ZIKV epidemic in the Santander and Norte de Santander departments (Santanderes) in Colombia were used to estimate cumulative incidence rates. Spatial analysis was performed to develop hot spot maps and to identify spatial topographic risk factors for infection. From January 1, 2016 to March 19, 2016, 11,515 cases of ZIKV were reported in Santanderes, with cumulative rates of 316.07 cases/100,000 population for the region (representing 18.5% of the cases of the country). Five municipalities (four in Norte de Santander) reported high incidence of ZIKV infection (>1,000 cases/100,000 pop); these municipalities are close to the border with Venezuela. Most of the cases reported occurred mainly in low altitude areas, and persistent hot spots were observed. Higher infection rates were reported in the Northeastern part of the study area. Use of risk maps can help guide decisions for the prevention and control of ZIKV. Hotspots on the Colombia-Venezuela border can have implications for international spread.

Differences in mortality characteristics in neonates with Down's syndrome

OBJECTIVE

Neonates with Down's syndrome (nDS) may have multiple medical issues that place them at increased risk for mortality during the newborn period. Goal of this study was to determine if there are differences in baseline characteristics, medical complications or procedures performed during hospitalization between nDS who survived versus those who died during initial hospitalization.

STUDY DESIGN

Data from 2000 to 2014 were reviewed using the Pediatric Health Information Systems (PHIS) database on all DS patients admitted to the hospital <30 days postnatal life. Baseline demographics, medical complications, procedures performed and mortality were recorded. Patients were divided into nDS patients who were discharged alive (nDS-a) versus nDS patients who died (nDS-d). Multivariate logistic analysis with odds ratios was performed to determine significant predictors of death. A P<0.05 was considered significant.

RESULTS

A total of 5737 nDS were evaluated. Overall mortality was 7.5% (431/5737). nDS-d were more likely than nDS-a to have a lower birth weight (1.0 (0.9 to 1.0)), presence of a diaphragmatic hernia (6.9 (1.9 to 25.1), or a cardiac diagnosis of a pulmonary venous abnormality (6.8 (1.9 to 24.4)), Ebstein's anomaly (3.2 (1.2 to 8.5)) or left-sided obstructive lesion (2.0 (1.3 to 3.0). nDS-d were more likely to develop hydrops (5.7 (3.5 to 9.5)) and necrotizing enterocolitis (1.7 (1.2 to 2.6)). In addition, nDS-d had significantly higher odds of requiring mechanical ventilation (20.7 (9.9 to 43.1)) or extracorporeal membrane oxygenation (8.7 (4.7 to 16.1)).

CONCLUSIONS

A number of characteristics, specifically certain cardiac diagnosis, place nDS at increased risk for mortality. Furthermore, development of specific medical complications or need for particular procedures increases the odds for mortality in nDS. Caregivers should be cognizant that they are taking care of a high-risk population nDS with an increased risk for mortality if these variables are present.