A state-based and optimal path dependant short-term flood planning

<p>Temporary flood protective defences (TFPD) are supplementary to permanent engineering solutions. In a flood event, asset managers are faced with a challenging task of deploying large-scale temporary defences at multiple locations. As the performance of temporary defences is sensitive to various uncertain weather condition factors, it is difficult to fix a single specific deployment plan as the optimal solution. This, moreover, leads to insufficient and/or underused defences on flood-affected locations. This paper describes a state-based (SB) mathematical modelling approach to deal with above challenge by adapting TFPD strategies consistently to short-term future as they unfold. We employ multistage stochastic and scenario tree to identify a set of alternative SB optimal paths for deployment planning. The proposed model is applied to nine flood-affected locations in Carlisle, northwest England. The results indicate that the inclusion of SB path-dependant solution strategy are beneficial for the flood asset manager faced with making short-term deployment planning decisions.</p>

Directional-Sensor Network Deployment Planning for Mobile-Target Search

In this paper, a novel time-phased directional-sensor network deployment strategy is presented for the mobile-target search problem, e.g., wilderness search and rescue (WiSAR). The proposed strategy uses probabilistic target-motion models combined with a variation of a standard direct search algorithm to plan the optimal locations of directional-sensors which maximize the likelihood of target detection. A linear sensing model is employed as a simplification for directional-sensor network deployment planning, while considering physical constraints, such as on-time sensor deliverability. Extensive statistical simulations validated our method. One such illustrative experiment is included herein to demonstrate the method’s operation. A comparative study was also carried out, whose summary is included in this paper, to highlight the tangible improvement of our approach versus three traditional deployment strategies: a uniform, a random, and a ring-of-fire type deployment, respectively.

A steady trickle-down from metro districts and improving epidemic-parameters characterize the increasing COVID-19 cases in India

Background. By mid-September of 2020, the number of daily new infections in India have crossed 95,000. To facilitate an intuition for the spatio-temporal development of the pandemic and to help resource deployment planning, we analyze and describe how the disease burden almost-predictably shifted from large metropolitan districts to sub-urban districts. Methods. We gathered the publicly available granular data from 186 different districts (equivalent of counties) on their COVID-19 infections and deaths during the 15 April to 31 August 2020 period. These districts presented an active case burden of 559,566 and a cumulative 2,715,656 infections as of August 31. The epidemiological data of these districts was fit to a susceptible-asymptomatic-infected-recovered-dead (SAIRD) model and the underlying epidemic parameters for each of these districts during the course of 4 months was estimated. We validated these parameters against known epidemiological characteristic distributions and analyzed them to understand their changes in space-time during the pandemic. Findings. The center of the burden of the current-active infections which on May 15 was in the large metro districts with most international access shifted continuously and smoothly shifted towards districts which could be accessed by domestic airports and by trains. A linear trend-analysis showed a continuous improvement in most epidemic parameters consistently across the districts with four categories of accessibility from an international travel perspective - large metro, metro, urban and sub-urban districts. The reproduction numbers improved from 1.77±0.58 on May 15 to 1.07± 0.13 on August 31 in large metro districts (p-Value of trend 0.0001053); and from 1.58±0.39 on May 15 to 0.94±0.11 on August 31 in sub-urban districts (p-Value of trend 0.0067). The recovery rate per infected person per day improved from 0.0581±0.009 on May 15 to 0.091±0.010 on August 31 in large metro districts (p-Value of trend 0.26.10^{-12}); and from 0.059±0.011 on May 15 to 0.100±0.010 on August 31 in sub-urban districts (p-Value of trend 0.12.10{-16}). The death rate of symptomatic individuals which includes the case-fatality-rate as well as the time from symptoms to death, consistently decreased from 0.0025±0.0014 on May 15 to 0.0013±0.0003 on August 31 in large metro districts (p-Value of trend 0.0010); and from 0.0018±0.0008 on May 15 to 0.0014±0.0003 on August 31 in sub-urban districts (p-Value of trend 0.2789. Interpretation. As the daily infections continue to rise at a national level, it is important to notice a `local-flattening' in larger metro districts, and a shift of the pandemic-burden towards smaller sized districts in a clear hierarchical fashion of accessibility from an international travel perspective. The pandemic burden shifting towards remotely accessible regions, with possibly lesser health care facilities, is a call for attention to the re-organization of resources.