A spatiotemporal analysis of COVID-19 transmission in Jakarta, Indonesia for pandemic decision support

With 25% confirmed cases of the country’s total number of coronavirus disease 2019 (COVID-19) on 31 January 2021, Jakarta has the highest confirmed cases of in Indonesia. The city holds a significant role as the centre of government and national economic activity for which pandemic have had a huge impact. Spatiotemporal analysis was employed to identify the current condition of disease transmission and to provide comprehensive information on the COVID-19 outbreak in Jakarta. We applied space-time analysis to visualise the pattern of COVID-19 hotspots in each time series. We also mapped area capacity of the referral hospitals covering the entire area of Jakarta to understand the hospital service range. This research was conducted in 4 stages: i) disease mapping; ii) spatial autocorrelation analysis; iii) space-time pattern analysis; and iv) areal capacity mapping. The analysis resulted in 144 sub-districts categorised as high vulnerability. Autocorrelation studies by Moran’s I identified cluster patterns and the emerging hotspot results indicated successful interventions as the number of hotspots fell in the first period of social restrictions. The results presented should be beneficial for policy makers.

Seismic Diffraction Analysis of a Fluid Escape Pipe beneath the Submarine Gas Bubble Plume in the Haima Cold Seep Area

Pipe structures are considered as fluid conduits beneath cold seeps. These structures have been observed in many geological settings and are widely accepted as the most critical pathway for fluid migration. One of such pipe structures in the Haima cold seep region is investigated herein. The pipe structure extends from below the BSR and reaches the seafloor. It is characterized by a string of events with short and strong seismic amplitudes, similar to the string of bead reflections (SBRs) associated with small-scale caves in carbonate reservoirs. This leads to the hypothesis that multiple small-scale bodies exist within the pipe structure. We test this hypothesis by analysis of diffraction waves and numerical seismic modeling. Travel time pattern analysis indicates that the diffractors within the pipe structure caused the rich diffraction waves on the shot records, and the reversed polarity indicates that the diffractors have a lower impedance than the surrounding sediments. These low-impedance bodies are interpreted as gas pockets within the pipe structures. Based on these interpretations, a conceptual model is proposed to describe the fluid migration process within the pipe. Briefly, we propose that gas pockets within the pipe structure could be analogue to the magma chambers located beneath volcanoes and this may provide a new insight into how gases migrate through the pipe structure and reach the seafloor.

Combining and comparing regional epidemic dynamics in Italy: Bayesian meta-analysis of compartmental models and model assessment via Global Sensitivity Analysis

During autumn 2020, Italy faced a second important SARS-CoV-2 epidemic wave. We explored the time pattern of the instantaneous reproductive number, R0(t), and estimated the prevalence of infections by region from August to December calibrating SIRD models on COVID19-related deaths, fixing at values from literature Infection Fatality Rate (IFR) and infection duration. A Global Sensitivity Analysis (GSA) was performed on the regional SIRD models. Then, we used Bayesian meta-analysis and meta-regression to combine and compare the regional results and investigate their heterogeneity. The meta-analytic R0(t) curves were similar in the Northern and Central regions, while a less peaked curve was estimated for the South. The maximum R0(t) ranged from 2.61 (North) to 2.15 (South) with an increase following school reopening and a decline at the end of October. Average temperature, urbanization, characteristics of family medicine and health care system, economic dynamism, and use of public transport could partly explain the regional heterogeneity. The GSA indicated the robustness of the regional R0(t) curves to different assumptions on IFR. The infectious period turned out to have a key role in determining the model results, but without compromising between-region comparisons.

Integrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan

The spatial–temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space–time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007–2016 as an example vector disease. The most significant clustering is evident during the years 2007–2008, 2010–2011, 2013, and 2016. Mostly, the clusters are found within the city’s central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics.

Quantifying the human influence on the intensity of extreme 1- and 5-day precipitation amounts at global, continental, and regional scales

This study provides a comprehensive analysis of the human contribution to the observed intensification of precipitation extremes at different spatial scales. We consider the annual maxima of the logarithm of 1-day (Rx1day) and 5-day (Rx5day) precipitation amounts for 1950–2014 over the global land area, four continents, and several regions, and compare observed changes with expected responses to external forcings as simulated by CanESM2 in a large-ensemble experiment and by multiple models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We use a novel detection and attribution analysis method that is applied directly to station data in the areas considered without prior processing such as gridding, spatial or temporal dimension reduction or transformation to unitless indices and uses climate models only to obtain estimates of the space-time pattern of extreme precipitation response to external forcing. The influence of anthropogenic forcings on extreme precipitation is detected over the global land area, three continental regions (western Northern Hemisphere, western Eurasia and eastern Eurasia), and many smaller IPCC regions, including C. North-America, E. Asia, E.C. Asia, E. Europe, E. North-America, N. Europe, and W. Siberia for Rx1day, and C. North-America, E. Europe, E. North-America, N. Europe, Russian-Arctic, and W. Siberia for Rx5day. Consistent results are obtained using forcing response estimates from either CanESM2 or CMIP6. Anthropogenic influence is estimated to have substantially decreased the approximate waiting time between extreme annual maximum events in regions where anthropogenic influence has been detected, which has important implications for infrastructure design and climate change adaptation policy.

Why Are Banks Exposed to Monetary Policy?

We propose a model of banks’ exposure to movements in interest rates and their role in the transmission of monetary shocks. Since bank deposits provide liquidity, higher interest rates allow banks to earn larger spreads on deposits. Therefore, if risk aversion is higher than one, banks’ optimal dynamic hedging strategy is to take losses when interest rates rise. This risk exposure can be achieved by a traditional maturity-mismatched balance sheet and amplifies the effects of monetary shocks on the cost of liquidity. The model can match the level, time pattern, and cross-sectional pattern of banks’ maturity mismatch. (JEL E43, E44, E51, E52, G21, G32)

Design and Implementation of Electromyography (EMG) based Real-Time Pattern Recognition model for Prosthetic hand Control

Surface electromyogram (EMG) signals are a key component in myoelectric control systems utilized in modern prosthetic devices. Despite extensive study into EMG gesture detection techniques for hand and arm gestures, most prosthetic devices rely on direct control approaches that are often confined to single movements. The purpose of this paper is to investigate various feature extraction techniques and to compare various machine learning algorithms, window sizes to identify the most suitable algorithm, window size for real-time gesture recognition. For this purpose, a publicly available pre-labeled 2-channel EMG dataset was used as EMG signals. Feature sets for each window size were extracted using various feature extraction techniques and fed into support vector machines, k-nearest neighbors, ensemble learning, and feed-forward artificial neural network (ANN) classifiers. The feed-forward neural networks classifier was determined to be the best classifier based on its accuracies, sizes, and prediction delays for each window size. The maximum accuracy of the feed-forward ANN classifier was ≈87% with a 300-millisecond window size. the use of the majority voting technique was considered in terms of the number of votes and the window sizes.

Optimal Placement and Operation of Chlorine Booster Stations: A Multi-Level Optimization Approach

Chlorine demand as a disinfectant for water utility impacts on unintended energy consumption from electrolysis manufacture; thus, diminishing the chlorine consumption also reduces the environmental impact and energy consumption. Problems of disinfectant distribution and uniformity in Water Distribution Networks (WDN) are associated with the exponential urban growth and the physical and biochemical difficulties within the network. This study optimizes Chlorine Booster Stations (CBS) location on a network with two main objectives; (1) to deliver minimal Free Residual Chlorine (FRC) throughout all demand nodes according to country regulations, and (2) to reduce day chlorine mass concentration supplied in the system by applying an hour time pattern in CBS, consequently associated economic, energy and environmental impacts complying with regulatory standards. The application is demonstrated on a real-world WDN modeled from Guanajuato, Mexico. The resulting optimal location and disinfectant dosage schedule in CBS provided insights on maintaining disinfectant residuals throughout all the WDN to prevent health issues and diminishing chlorine consumption.

Pattern recognition on FPGA for aerospace applications

This paper presents a low power near real-time pattern recognition technique based on Mathematical Morphology-MM implemented on FPGA (Field Programmable Gate Array). The key to the success of this approach concerns the advantages of machine learning paradigm applied to the translation invariant template-matching operators from MM. The paper shows that compositions of simple elementary operators from Mathematical Morphology based on ELUTs (Elementary Look-Up Tables) are very suitable to embed in FPGA hardware. The paper also shows the development techniques regarding all mathematical modeling for computer simulation and system generating models applied for hardware implementation using FPGA chip. In general, image processing on FPGAs requires low-level description of desired operations through Hardware Description Language-HDL, which uses high complexity to describe image operations at pixel level. However, this work presents a reconfiguring pattern recognition device implemented directly in FPGA from mathematical modeling simulation under Matlab/Simulink/System Generator environment. This strategy has reduced the hardware development complexity. The device will be useful mainly when applied on remote sensing tasks for aerospace missions using passive or active sensors.