Challenges in Tracking the Risk of COVID-19 in Bangladesh: Evaluation of A Novel Method

Identifying actual risk zones in a country where the overall test positive rate (TPR) is higher than 5% is crucial to contain the pandemic. However, TPR-based risk zoning methods are debatable since they do not consider the rate of infection in an area and thus, it has been observed to overestimate the risk. Similarly, the rate of infection in an area has been noticed to underestimate the risk of COVID-19 spreading for the zones with higher TPR. In this article, we discuss the shortcomings of currently available risk zoning methods that are followed in the lower-middle-income countries (LMIC), especially in Bangladesh. We then propose to determine a risk zone by combining the rate of infection with TPR and effective reproduction number, R_t in a distinct manner from existing methods. We evaluate the efficacy of the proposed method with respect to the mass-movement events and show its application to track the evolution of COVID-19 pandemic by identifying the risk zones over time. Demo website for the visualization of the analysis can be found at: http://erdos.dsm.fordham.edu:3000/.

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COVID-19 basic reproduction number and assessment of initial suppression policies in Costa Rica

Mathematical Modelling of Natural Phenomena ◽

10.1051/mmnp/2020019 ◽

2020 ◽

Vol 15 ◽

pp. 32 ◽

Cited By ~ 5

Author(s):

Luis Fernando Chaves ◽

Lisbeth A. Hurtado ◽

Melissa Ramírez Rojas ◽

Mariel D. Friberg ◽

Rodrigo Marín Rodríguez ◽

...

Keyword(s):

Costa Rica ◽

Health Systems ◽

Basic Reproduction Number ◽

Reproduction Number ◽

Early Stage ◽

Growth Curves ◽

Emerging Pathogens ◽

Middle Income ◽

Universal Healthcare ◽

Middle Income Countries

SARS-COV-2 is the most recent from a series of emerging pathogens stressing national health systems. Initially restricted to Hubei province in China, COVID-19, the disease caused by SARS-COV-2 has become pandemic, reaching almost every nation on our planet. Here, we present an estimate of the Basic Reproduction Number (R0) for this disease based on confirmed cases recorded during the initial 30 days of transmission. Based on local transmission data for the six initial days of transmission, we estimated (mean ± SE) R0 = 2.58 ± 2.43. R0 was reduced by up to 56% to R0 = 1.12 ± 0.02 following suppression measures in place by April 4th, 2020. Independent estimates for the time-varying reproduction number (Rt) based on the serial interval distribution estimated for China showed that after 30 days, Rt decreased reaching a value of 0.914 ± 0.104 on April 4th, 2020. In this study, we also describe the suppression strategies in place in Costa Rica and compare their impacts with those implemented in Panamá and Uruguay, provided these three middle-income countries have similar demographic and economic indicators. However, these three countries have structurally different health systems and have resorted to different suppression measures against COVID-19. We compare the early epidemic growth curves in the three countries using an exponential deceleration model. We found the lowest epidemic growth rate in Costa Rica, followed by Panamá and then Uruguay, while the highest deceleration was observed in Uruguay, followed by Costa Rica and Panamá. We discuss how the unified, universal healthcare system of Costa Rica has been vital to successfully manage the early stage of the COVID-19 epidemic and call for the developments of precision public health tools to further improve epidemic health surveillance in Costa Rica.

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Object Detection and Instance Segmentation in Chest X-rays for Tuberculosis Screening

International Journal of Transdisciplinary Artificial Intelligence ◽

10.35708/tai1870-126250 ◽

2021 ◽

Vol 3 (1) ◽

pp. 1-24

Author(s):

Terence Griffin ◽

Yu Cao ◽

Benyuan Liu ◽

Maria J. Brunette ◽

Xinzi Sun

Keyword(s):

Object Detection ◽

False Positive Rate ◽

X Rays ◽

Tuberculosis Screening ◽

Screening Process ◽

Middle Income ◽

Middle Income Countries ◽

Positive Rate ◽

Object Level ◽

Instance Segmentation

Tuberculosis (TB) is a highly contagious disease leading to the deaths of approximately 2 million people annually. TB primarily affects the lungs and is spread through the air when people cough, sneeze, or spit. Providing healthcare professionals with better information, at a faster pace, is essential for combating this disease, especially in Low and Middle Income Countries (LMICs) with resource-constrained health systems. In this paper we describe how using convolution neural networks (CNNs) with an object level annotated dataset of chest X-rays (CXRs) allows us to identify the location of pulmonary issues indicative of TB. We compare the performance of Faster R-nobreakdash-CNN, Mask R-nobreakdash-CNN, Cascade versions of each, and SOLOv2, demonstrating reasonable results with a small dataset. We present a method to reduce the false positive rate by comparing the location of a detected object with the known location of areas where the detected class is likely to occur in the lung. Our results show that object detection and instance segmentation of CXRs can be achieved with a dataset of high-quality, object level annotations, and could be used as part of an automated TB screening process. This work has the potential to improve the speed of TB diagnosis in LMICs, if properly integrated into the healthcare system and adapted to existing clinical workflows and local regulations.

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Flood Risk Zoning of Satluj River Basin, Himachal Pradesh, India

International Letters of Natural Sciences ◽

10.18052/www.scipress.com/ilns.40.6 ◽

2015 ◽

Vol 40 ◽

pp. 6-15 ◽

Cited By ~ 1

Author(s):

Sandeep Kumar ◽

Santosh

Keyword(s):

At Risk ◽

Land Use ◽

River Basin ◽

Flood Risk ◽

Low Risk ◽

Risk Zone ◽

Risk Zoning ◽

Risk Zones ◽

Satluj River ◽

Very High

Increasing intensity and frequency of rainfall coupled with gradual retreating of glaciers due to climate change in Himalayan region likely to increase the risk of floods. A better understanding of risk zones which are vulnerable to flood disasters can be evolved from the detailed studies on slope, geomorphology and land use/ land cover pattern. Information of these parameters is an important input for the identification of vulnerable areas. Flood risk maps provide useful information about places that may be at risk from flooding. It offers a cost-effective solution for planning, management and mitigation strategies in risky areas. Traditional methods of flood risk mapping are based on ground surveys and aerial observations, but when the phenomenon is widespread, such methods are time consuming and expensive. The possible combination of DEM and other maps of area using an overlay operation method within the Geographical Information System (GIS) platform can lead to derivation and the understanding of spatial association between various parameters which could be used to predict flood risk zones. The study area i.e. Satluj River Basin has been broadly divided into five risk zones viz., very low, low, moderate, high and very high which helped to differentiate between areas that are at risk of different intensities of flood. The very high flood risk zone covers only 3.25 % of total study area, while the very low risk zone covers 13.63 %. The area falls within the very high and high risk constitutes 9.52 % of total basin area. Domain of moderate risk covers an area of 30.66 %. But the maximum area of river basin is constituted by low risk zone i.e. 46.19 %. Identification of such zones will help in timely adopting of mitigation and adaptation measures. Preparation of flood risk zoning maps also helps in regulating indiscriminate and unplanned land use practices in risky areas.

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A Method for Repeated, Longitudinal Sampling of Individual Aedes aegypti for Transmission Potential of Arboviruses

Insects ◽

10.3390/insects12040292 ◽

2021 ◽

Vol 12 (4) ◽

pp. 292

Author(s):

E. Handly Mayton ◽

Heather M. Hernandez ◽

Christopher J. Vitek ◽

Rebecca C. Christofferson

Keyword(s):

Vector Competence ◽

Vero Cells ◽

Middle Income ◽

Transmission Potential ◽

Middle Income Countries ◽

Complex Process ◽

Novel Method ◽

Longitudinal Sampling ◽

Over Time ◽

Biting Behavior

Mosquito-borne viruses are the cause of significant morbidity and mortality worldwide, especially in low- and middle-income countries. Assessing risk for viral transmission often involves characterization of the vector competence of vector–virus pairings. The most common determination of vector competence uses discreet, terminal time points, which cannot be used to investigate variation in transmission aspects, such as biting behavior, over time. Here, we present a novel method to longitudinally measure individual biting behavior and Zika virus (ZIKV) transmission. Individual mosquitoes were exposed to ZIKV, and from 9 to 24 days post-exposure, individuals were each offered a 180 μL bloodmeal every other day. Biting behavior was observed and characterized as either active probing, feeding, or no bite. The bloodmeal was then collected, spun down, serum collected, and tested for ZIKV RNA via qRT-PCR to determine individuals’ vector competence over time. This included whether transmission to the bloodmeal was successful and the titer of expectorated virus. Additionally, serum was inoculated onto Vero cells in order to determine infectiousness of positive recovered sera. Results demonstrate heterogeneity in not only biting patterns but expectorated viral titers among individual mosquitoes over time. These findings demonstrate that the act of transmission is a complex process governed by mosquito behavior and mosquito–virus interaction, and herein we offer a method to investigate this phenomenon.

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A novel index-based decision support toolkit for safe reopening following a generalized lockdown in low and middle-income countries

Scientific Reports ◽

10.1038/s41598-021-93415-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Abu S. Shonchoy ◽

Khandker S. Ishtiaq ◽

Sajedul Talukder ◽

Nasar U. Ahmed ◽

Rajiv Chowdhury

Keyword(s):

Low Income ◽

Large Scale ◽

Epidemiological Data ◽

World Health ◽

Optimal Timing ◽

Middle Income ◽

Capacity Limits ◽

Middle Income Countries ◽

Positive Rate ◽

Health Organization

AbstractWhile the effectiveness of lockdowns to reduce Coronavirus Disease-2019 (COVID-19) transmission is well established, uncertainties remain on the lifting principles of these restrictive interventions. World Health Organization recommends case positive rate of 5% or lower as a threshold for safe reopening. However, inadequate testing capacity limits the applicability of this recommendation, especially in the low-income and middle-income countries (LMICs). To develop a practical reopening strategy for LMICs, in this study, we first identify the optimal timing of safe reopening by exploring accessible epidemiological data of 24 countries during the initial COVID-19 surge. We find that a safe opening can occur two weeks after the crossover of daily infection and recovery rates while maintaining a negative trend in daily new cases. Epidemiologic SIRM model-based example simulation supports our findings. Finally, we develop an easily interpretable large-scale reopening (LSR) index, which is an evidence-based toolkit—to guide/inform reopening decision for LMICs.

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