Evaluating Temporal Factors in Combined Interventions of Workforce Shift and School Closure for Mitigating the Spread of Influenza

10.1371/journal.pone.0032203PLoS ONE7

Comparability of Results from Pair and Classical Model Formulations for Different Sexually Transmitted Infections

The “classical model” for sexually transmitted infections treats partnerships as instantaneous events summarized by partner change rates, while individual-based and pair models explicitly account for time within partnerships and gaps between partnerships. We compared predictions from the classical and pair models over a range of partnership and gap combinations. While the former predicted similar or marginally higher prevalence at the shortest partnership lengths, the latter predicted self-sustaining transmission for gonorrhoea (GC) and Chlamydia (CT) over much broader partnership and gap combinations. Predictions on the critical level of condom use (Cc) required to prevent transmission also differed substantially when using the same parameters. When calibrated to give the same disease prevalence as the pair model by adjusting the infectious duration for GC and CT, and by adjusting transmission probabilities for HIV, the classical model then predicted much higher Cc values for GC and CT, while Cc predictions for HIV were fairly close. In conclusion, the two approaches give different predictions over potentially important combinations of partnership and gap lengths. Assuming that it is more correct to explicitly model partnerships and gaps, then pair or individual-based models may be needed for GC and CT since model calibration does not resolve the differences

Smart interpretable model (SIM) enabling subject matter experts in rule generation

Current Artificial Intelligence (AI) technologies are widely regarded as black boxes, whose internal structures are not inherently transparent, even though they provide powerful prediction capabilities. Having a transparent model that enables users to understand its inner workings allows them to appreciate the learning and inference process, leading to trust and higher confidence in the model. While methods that help with interpretability have been created, most of them require the user to have a certain level of AI knowledge and do not allow a user to fine-tune them based on prior knowledge. In this paper, we present a smart interpretable model (SIM) framework that requires little to no AI knowledge and can be used to create a set of fuzzy IF-THEN rules along with its corresponding membership functions at ease. The framework also allows users to incorporate prior knowledge during various steps in the framework and generates comprehensive insights summarized from rules and samples, allowing users to identify anomalous rules, feature contributions of each sample and confidence level for each rule. We demonstrate these capabilities and compare our model to other existing rule-based models using various datasets that have been used for rule-based model validations. Validations are then done in terms of performance and whether the rules that are generated by SIM are similar to the rules generated by other more recent rule-based models.This study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from Rolls-Royce Singapore Pte Ltd

A contact-network-based simulation model for evaluating interventions under “what-if” scenarios in epidemic

Infectious disease pandemics/epidemics have been serious concerns worldwide. Simulations for public health interventions are practically helpful in assisting policy makers to make wise decisions to control and mitigate the spread of infectious diseases. In this paper, we present our contact network based simulation model, which is designed to accommodate various 'what-if' scenarios under single and combined interventions. With the incorporation of parallel computing and optimization techniques, our model is able to reflect the dynamics of disease spread in a realistic social contact network based on Singapore city, simulating combined intervention strategies as well as control effect at different levels of a social component. The framework of our model and experimental results show that it is a useful tool for epidemiological study and public health policy planning

Predictions from the classical and pair model formulations for the steady-state <i>π^s</i>of GC/CT (A and B), and the peak <i>π^p</i> of HIV with and without cofactor enhancement (C and D).

<p>The horizontal axes give partnership length in days while the vertical axes give <i>π</i>. The different lines denote predictions from using gap lengths () of 1 day, 7 days, 30 days and 90 days. The inset in each figure magnifies crossover point, if any, in the region where the classical and pair models diverge in <i>π</i> predictions. Models in (A) and (C) are unable to provide predictions at a gap length of 90 days.</p

Smart Robust Feature Selection (SoFt) for imbalanced and heterogeneous data

Designing a smart and robust predictive model that can deal with imbalanced data and a heterogeneous set of features is paramount to its widespread adoption by practitioners. By smart, we mean the model is either parameter-free or works well with default parameters, avoiding the challenge of parameter tuning. Furthermore, a robust model should consistently achieve high accuracy regardless of any dataset (imbalance, heterogeneous set of features) or domain (such as medical, financial). To this end, a computationally inexpensive and yet robust predictive model named smart robust feature selection (SoFt) is proposed. SoFt involves selecting a learning algorithm and designing a filtering-based feature selection algorithm named multi evaluation criteria and Pareto (MECP). Two state-of-the-art gradient boosting methods (GBMs), CatBoost and H2O GBM, are considered potential candidates for learning algorithms. CatBoost is selected over H2O GBM due to its robustness with both default and tuned parameters. The MECP uses multiple parameter-free feature scores to rank the features. SoFt is validated against CatBoost with a full feature set and wrapper-based CatBoost. SoFt is robust and consistent for imbalanced datasets, i.e., average value and standard deviation of log loss are low across different folds of K-fold cross-validation. Features selected by MECP are also consistent, i.e., features selected by SoFt and wrapper-based CatBoost are consistent across different folds, demonstrating the effectiveness of MECP. For balanced datasets, MECP selects too few features, and hence, the log loss of SoFt is significantly higher than CatBoost with a full feature set

The Emergence of Urban Land Use Patterns Driven by Dispersion and Aggregation Mechanisms

<div><p>We employ a cellular-automata to reconstruct the land use patterns of cities that we characterize by two measures of spatial heterogeneity: (a) a variant of <i>spatial entropy</i>, which measures the spread of residential, business, and industrial activity sectors, and (b) an <i>index of dissimilarity</i>, which quantifies the degree of spatial mixing of these land use activity parcels. A minimalist and bottom-up approach is adopted that utilizes a limited set of three parameters which represent the forces which determine the extent to which each of these sectors spatially aggregate into clusters. The dispersion degrees of the land uses are governed by a fixed pre-specified power-law distribution based on empirical observations in other cities. Our method is then used to reconstruct land use patterns for the city state of Singapore and a selection of North American cities. We demonstrate the emergence of land use patterns that exhibit comparable visual features to the actual city maps defining our case studies whilst sharing similar spatial characteristics. Our work provides a complementary approach to other measures of urban spatial structure that differentiate cities by their land use patterns resulting from bottom-up dispersion and aggregation processes.</p></div

Data from Lycke et al. [45] on case contact pairs for gonorrhoea and Chlamydia

1<p>Chlamydia.</p>2<p>Gonorrhoea.</p

Critical level of condom use (<i>C_c</i>) predicted to prevent self-sustaining GC/CT and HIV transmission for the pair (A to D), classical uncalibrated (E to H), and classical model following calibration of <i>π</i> to the pair model output (I to L).

<p>The horizontal axes give partnership length in days while the vertical axes give gap length in days. <i>C_c</i> values are denoted by a gradient of colours as indicated; values of 0% demarcate the most extreme combination of partnership and gap lengths which supports self-sustaining transmission, while values above 100% (up to a theoretical maximum of 111% since condoms are assumed to be only 90% effective in preventing transmission) show partnership and gap combinations where consistent condom use is insufficient to prevent self-sustaining transmission.</p