Relative importance of VECTRI model parameters in the malaria disease transmission and prevalence.

In this study, a sensitivity analysis on a VECTRI dynamical model of malaria transmission is investigated to determine the relative importance of model parameters to disease transmission and prevalence. Apart from being most climatic prone, Odisha is a highly endemic state for malaria in India. The lack in sufficient modeling studies severely impacts the malarial process studies which further hinder the possibility of malaria early warning systems and preventive measures to be undertaken beforehand. Therefore, modeling studies and investigating the relationship between malaria transmission process studies and associated climatic factors are the need of the hour. Environmental conditions have pronounced effects on the malaria transmission dynamics and abundance of the poikilothermic vectors, but the exact relationship of sensitivity for these parameters is not well established. Sensitivity analysis is a useful tool for ascertaining model responses to different input variables. Therefore, in order to perform the requisite study, a dynamical model, VECTRI, is utilized. The study period ranges from 2000 to 2013, where several sensitivity tests are performed using different model parameters such as infiltration and evaporation rate loss of ponds, degree-days for parasite development, threshold temperature for parasite development, threshold temperature for egg development in the vector, and maximum and minimum temperature for larvae survival. The experiments suggest that the lower value of minimum temperature for larvae survival (rlarv_tmin), i.e., 16 °C, provides higher vector density and entomological inoculation rate (EIR) values. EIR reaches its maximum, when the threshold temperature for parasite development (rtsporo) is 22 °C and degree-days for parasite development (dsporo) is 8 degree-days. No change is observed in the vector density; even when rtsporo is 30 °C, values of EIR are close to 0. A successive increment of infiltration and evaporation rate loss of ponds (rwaterfrac evap126) values from 130 to 200 mm/day result in approximately 5% consistent decline in vector density and EIR. The study concludes that the most sensitive parameters are dsporo, rlarv_tmin, and rwaterfrac evap126. The VECTRI model is rather insensitive to maximum temperature for larvae survival (rlarv_tmin) for vector density and EIR variables. Further certain modifications and improvements are required in VECTRI to predict out variables like vector density and EIR more accurately in highly endemic region.

Potential future malaria transmission in Odisha due to climate change.

Future projections of malaria transmission is made for Odisha, a highly endemic region of India, through numerical simulations using the VECTRI dynamical model. The model is forced with bias-corrected temperature and rainfall from a global climate model (CCSM4) for the baseline period 1975-2005 and for the projection periods 2020s, 2050s, and 2080s under RCP8.5 emission scenario. The temperature, rainfall, mosquito density and entomological inoculation rate (EIR), generated from the VECTRI model are evaluated with the observation and analyzed further to estimate the future malaria transmission over Odisha on a spatio-temporal scale owing to climate change. Our results reveal that the malaria transmission in Odisha as a whole during summer and winter monsoon seasons may decrease in future due to the climate change except in few districts with the high elevations and dense forest regions such as Kandhamal, Koraput, Raygada and Mayurbhanj districts where an increase in malaria transmission is found. Compared to the baseline period, mosquito density shows decrease in most districts of the south, southwest, central, north and northwest regions of Odisha in 2030s, 2050s and 2080s. An overall decrease in malaria transmission of 20-40% (reduction in EIR) is seen during the monsoon season (June-Sept) over Odisha with the increased surface temperature of 3.5-4 °C and with the increased rainfall of 20-35% by the end of the century with respect to the baseline period. Furthermore, malaria transmission is likely to reduce in future over most of the Odisha regions with the increase in future warm and cold nights temperatures.

A Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques.

The rapid growth of cloud computing environment with many clients ranging from personal users to big corporate or business houses has become a challenge for cloud organizations to handle the massive volume of data and various resources in the cloud. Inefficient management of resources can degrade the performance of cloud computing. Therefore, resources must be evenly allocated to different stakeholders without compromising the organization's profit as well as users' satisfaction. A customer's request cannot be withheld indefinitely just because the fundamental resources are not free on the board. In this paper, a combined resource allocation security with efficient task scheduling in cloud computing using a hybrid machine learning (RATS-HM) technique is proposed to overcome those problems. The proposed RATS-HM techniques are given as follows: First, an improved cat swarm optimization algorithm-based short scheduler for task scheduling (ICS-TS) minimizes the make-span time and maximizes throughput. Second, a group optimization-based deep neural network (GO-DNN) for efficient resource allocation using different design constraints includes bandwidth and resource load. Third, a lightweight authentication scheme, i.e., NSUPREME is proposed for data encryption to provide security to data storage. Finally, the proposed RATS-HM technique is simulated with a different simulation setup, and the results are compared with state-of-art techniques to prove the effectiveness. The results regarding resource utilization, energy consumption, response time, etc., show that the proposed technique is superior to the existing one.

Numerical Modeling of the Dynamics of Malaria Transmission in a Highly Endemic Region of India.

Using a dynamical model (VECTRI) for malaria transmission that accounts for the influence of population and climatic conditions, malaria transmission dynamics is investigated for a highly endemic region (state of Odisha) in India. The model is first calibrated over the region, and subsequently numerical simulations are carried out for the period 2000-2013. Using both model and observations we find that temperature, adult mosquito population, and infective biting rates have increased over this period, and the malaria vector abundance is higher during the summer monsoon season. Regionally, the intensity of malaria transmission is found to be higher in the north, central and southern districts of Odisha where the mosquito populations and the number of infective bites are more and mainly in the forest or mountainous ecotypes. We also find that the peak of the malaria transmission occurs when the monthly mean temperature is in the range of ~28-29 °C, and monthly rainfall accumulation in the range of ~200-360 mm.

Considerations for double-hand replantation in a resource-constrained healthcare facility.

Bilateral-hand amputation is extremely rare and double-hand replantation is even rarer. Only one case of successful double replantation at arm level has been reported from India. We present a case of double-hand replantation at proximal palmar level in a young adult executed in a small nursing home. The patient presented 5 h after injury with limbs preserved well in ice. There were difficulties in executing such an unusual case in a small nursing home set-up. The patient is performing his activities of daily living and basic functions independently. We share our experience of this double-hand replantation with special emphasis on problems encountered.