Observation of stochastic coherence in coupled map lattices

Chaotic evolution of structures in a coupled map lattice driven by identical noise on each site is studied (a structure is a group of neighboring lattice sites for which values of the dynamical variable follow a certain predefined pattern). The number of structures is seen to follow a power-law decay with length of the structure for a given noise strength. An interesting phenomenon, which we call stochastic coherence, is reported in which a rise of bell-shaped type in abundance and lifetimes of these structures is observed for a range of noise-strength values

Percolation of finite-sized objects on a lattice

We study the percolation of finite-sized objects on two- and three-dimensional lattices. Our motivation stems, on one hand, from some recent interesting experimental results on transport properties of impurity-doped oxide perovskites, and on the other hand from the theoretical appeal that this problem presents. Our system exhibits a well-defined percolation threshold. We estimate the size of magnetic polarons, believed to be the carriers of the above-mentioned transport. We have also obtained two critical exponents for our model, which characterize its universality class

Simple temporal models for ecological systems with complex spatial patterns

Spatial patterns are ubiquitous in nature. Because these patterns modify the temporal dynamics and stability properties of population densities at a range of spatial scales, their effects must be incorporated in temporal ecological models that do not represent space explicitly. We demonstrate a connection between a simple parameterization of spatial effects and the geometry of clusters in an individual-based predator–prey model that is both nonlinear and stochastic. Specifically we show that clusters exhibit a power-law scaling of perimeter to area with an exponent close to unity. In systems with a high degree of patchiness, similar power-law scalings can provide a basis for applying simple temporal models that assume well-mixed conditions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72011/1/j.1461-0248.2002.00334.x.pd

Predictability of epidemic malaria under non-stationary conditions with process-based models combining epidemiological updates and climate variability.

BACKGROUND: Previous studies have demonstrated the feasibility of early-warning systems for epidemic malaria informed by climate variability. Whereas modelling approaches typically assume stationary conditions, epidemiological systems are characterized by changes in intervention measures over time, at scales typically longer than inter-epidemic periods. These trends in control efforts preclude simple application of early-warning systems validated by retrospective surveillance data; their effects are also difficult to distinguish from those of climate variability itself. METHODS: Rainfall-driven transmission models for falciparum and vivax malaria are fitted to long-term retrospective surveillance data from four districts in northwest India. Maximum-likelihood estimates (MLEs) of model parameters are obtained for each district via a recently introduced iterated filtering method for partially observed Markov processes. The resulting MLE model is then used to generate simulated yearly forecasts in two different ways, and these forecasts are compared with more recent (out-of-fit) data. In the first approach, initial conditions for generating the predictions are repeatedly updated on a yearly basis, based on the new epidemiological data and the inference method that naturally lends itself to this purpose, given its time-sequential application. In the second approach, the transmission parameters themselves are also updated by refitting the model over a moving window of time. RESULTS: Application of these two approaches to examine the predictability of epidemic malaria in the different districts reveals differences in the effectiveness of intervention for the two parasites, and illustrates how the 'failure' of predictions can be informative to evaluate and quantify the effect of control efforts in the context of climate variability. The first approach performs adequately, and sometimes even better than the second one, when the climate remains the major driver of malaria dynamics, as found for Plasmodium vivax for which an effective clinical intervention is lacking. The second approach offers more skillful forecasts when the dynamics shift over time, as is the case of Plasmodium falciparum in recent years with declining incidence under improved control. CONCLUSIONS: Predictive systems for infectious diseases such as malaria, based on process-based models and climate variables, can be informative and applicable under non-stationary conditions

Maternal mortality-a three-year retrospective study in a rural medical college of India

Background: Maternal mortality is a tragic event as family revolves around a mother. The deadly obstetrical triad of hemorrhage, preeclampsia and infection has accounted for a third of all deaths. This study was conducted to assess maternal mortality ratio, demographic profile and causes of maternal death.Methods: This retrospective longitudinal study was conducted in the department of obstetrics and gynecology for a period of three years from 1st January 2018-31st December, 2020. Total no of death during this period was 134.Records of deaths and demographic profiles were retrieved from the medical record library of aforesaid hospital.Results: There were 134 maternal deaths amongst 56815 live births with MMR 235.85. The majority of deaths were in 20-29 year of age group and most of the deaths seen in multigravida. The 91.79% death was observed within the 24 hours and after 72 hours. Eclampsia, preeclampsia and hemorrhage were leading cause of maternal death seen in the study.Conclusions: Maternal mortality is higher than national MMR. Majority of maternal death were preventable by proper antenatal care, early detection of high-risk pregnancies and their timely referral to tertiary care centre

Seasonality in the migration and establishment of H3N2 Influenza lineages with epidemic growth and decline

Background: Influenza A/H3N2 has been circulating in humans since 1968, causing considerable morbidity and mortality. Although H3N2 incidence is highly seasonal, how such seasonality contributes to global phylogeographic migration dynamics has not yet been established. Results: Incorporating seasonally varying migration rates improves the modeling of migration. In our global model, windows of increased immigration map to the seasonal timing of epidemic spread, while windows of increased emigration map to epidemic decline. Seasonal patterns also correlate with the probability that local lineages go extinct and fail to contribute to long term viral evolution, as measured through the trunk of the phylogeny. However, the fraction of the trunk in each community was found to be better determined by its overall human population size Conclusions: Seasonal migration and rapid turnover within regions is sustained by the invasion of 'fertile epidemic grounds' at the end of older epidemics. Thus, the current emphasis on connectivity, including air-travel, should be complemented with a better understanding of the conditions and timing required for successful establishment.Models which account for migration seasonality will improve our understanding of the seasonal drivers of influenza,enhance epidemiological predictions, and ameliorate vaccine updating by identifying strains that not only escape immunity but also have the seasonal opportunity to establish and spread. Further work is also needed on additional conditions that contribute to the persistence and long term evolution of influenza within the human population,such as spatial heterogeneity with respect to climate and seasonalityComment: in BMC Evolutionary Biology 2014, 1