A global examination of ecological niche modeling to predict emerging infectious diseases: a systematic review

Introduction

As emerging infectious diseases (EIDs) increase, examining the underlying social and environmental conditions that drive EIDs is urgently needed. Ecological niche modeling (ENM) is increasingly employed to predict disease emergence based on the spatial distribution of biotic conditions and interactions, abiotic conditions, and the mobility or dispersal of vector-host species, as well as social factors that modify the host species' spatial distribution. Still, ENM applied to EIDs is relatively new with varying algorithms and data types. We conducted a systematic review (PROSPERO: CRD42021251968) with the research question: What is the state of the science and practice of estimating ecological niches via ENM to predict the emergence and spread of vector-borne and/or zoonotic diseases?

Methods

We searched five research databases and eight widely recognized One Health journals between 1995 and 2020. We screened 383 articles at the abstract level (included if study involved vector-borne or zoonotic disease and applied ENM) and 237 articles at the full-text level (included if study described ENM features and modeling processes). Our objectives were to: (1) describe the growth and distribution of studies across the types of infectious diseases, scientific fields, and geographic regions; (2) evaluate the likely effectiveness of the studies to represent ecological niches based on the biotic, abiotic, and mobility framework; (3) explain some potential pitfalls of ENM algorithms and techniques; and (4) provide specific recommendation for future studies on the analysis of ecological niches to predict EIDs.

Results

We show that 99% of studies included mobility factors, 90% modeled abiotic factors with more than half in tropical climate zones, 54% modeled biotic conditions and interactions. Of the 121 studies, 7% include only biotic and mobility factors, 45% include only abiotic and mobility factors, and 45% fully integrated the biotic, abiotic, and mobility data. Only 13% of studies included modifying social factors such as land use. A majority of studies (77%) used well-recognized ENM algorithms (MaxEnt and GARP) and model selection procedures. Most studies (90%) reported model validation procedures, but only 7% reported uncertainty analysis.

Discussion

Our findings bolster ENM to predict EIDs that can help inform the prevention of outbreaks and future epidemics.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier (CRD42021251968).

A non-expensive bidimensional assessment can detect subtle alterations in gait performance in people in the early stages of Parkinson's disease

Background

Gait is one of the activities most affected by the symptoms of Parkinson's disease and may show a linear decline as the disease progresses. Early assessment of its performance through clinically relevant tests is a key factor in designing efficient therapeutic plans and procedures, which can be enhanced using simple and low-cost technological instruments.

Objective

To investigate the effectiveness of a two-dimensional gait assessment to identify the decline in gait performance associated with Parkinson's disease progression.

Methods

One hundred and seventeen people with Parkinson's disease, classified between early and intermediate stages, performed three clinical gait tests (Timed Up and Go, Dynamic Gait Index, and item 29 of the Unified Parkinson's Disease Rating Scale), in addition to a six-meter gait test recorded by a two-dimensional movement analysis software. Based on variables generated by the software, a gait performance index was created, allowing a comparison between its results with the results obtained by clinical tests.

Results

There were differences between sociodemographic variables directly related to the evolution of Parkinson's disease. Compared to clinical tests, the index proposed to analyze gait showed greater sensitivity and was able to differentiate the first three stages of disease evolution (Hoehn and Yahr I and II: p = 0.03; Hoehn and Yahr I and III: p = 0.00001; Hoehn and Yahr II and III: p = 0.02).

Conclusion

Based on the index provided by a two-dimensional movement analysis software that uses kinematic gait variables, it was possible to differentiate the gait performance decline among the three first stages of Parkinson's disease evolution. This study offers a promising possibility of early identification of subtle changes in an essential function of people with Parkinson's disease.

Statistical approaches using longitudinal biomarkers for disease early detection: A comparison of methodologies

Early detection of clinical outcomes such as cancer may be predicted using longitudinal biomarker measurements. Tracking longitudinal biomarkers as a way to identify early disease onset may help to reduce mortality from diseases like ovarian cancer that are more treatable if detected early. Two disease risk prediction frameworks, the shared random effects model (SREM) and the pattern mixture model (PMM) could be used to assess longitudinal biomarkers on disease early detection. In this article, we studied the discrimination and calibration performances of SREM and PMM on disease early detection through an application to ovarian cancer, where early detection using the risk of ovarian cancer algorithm (ROCA) has been evaluated. Comparisons of the above three approaches were performed via analyses of the ovarian cancer data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Discrimination was evaluated by the time-dependent receiver operating characteristic curve and its area, while calibration was assessed using calibration plot and the ratio of observed to expected number of diseased subjects. The out-of-sample performances were calculated via using leave-one-out cross-validation, aiming to minimize potential model overfitting. A careful analysis of using the biomarker cancer antigen 125 for ovarian cancer early detection showed significantly improved discrimination performance of PMM as compared with SREM and ROCA, nevertheless all approaches were generally well calibrated. Robustness of all approaches was further investigated in extensive simulation studies. The improved performance of PMM relative to ROCA is in part due to the fact that the biomarker measurements were taken at a yearly interval, which is not frequent enough to reliably estimate the changepoint or the slope after changepoint in cases under ROCA.

Quantification of population benefit in evaluation of biomarkers: practical implications for disease detection and prevention

BACKGROUND

With the rapid development of "-omic" technologies, an increasing number of purported biomarkers have been identified for cancer and other diseases. The process of identifying those that are most promising and validating them for use at the population level for prevention and early detection is a critical next step in achieving significant health benefits.

METHODS

In this paper, we propose that in order to effectively translate biomarkers for practical clinical use, it is important to distinguish and quantify the differences between the use of biomarkers and other risk factors to identify preventive interventions versus their use in disease risk prediction and early detection. We developed mathematical models for quantitatively evaluating risk and benefit in use of biomarkers for disease prevention or early detection. Simple numerical examples were used to demonstrate the potential applications of the models for various types of data.

RESULTS

We propose an index which takes into account potential adverse consequences of biomarker-driven interventions - the 'naïve' ratio of population benefit (RPB) - to facilitate evaluating the potential impact of biomarkers on cancer prevention and personalized medicine. The index RPB is developed for both binary and continuous biomarkers/risk factors. Examples with computational analyses are presented in the paper to contrast the differences in using biomarkers/risk factors for prevention and early detection.

CONCLUSIONS

Integrating epidemiologic knowledge into clinical decision making is a key step to translate new biomarkers/risk factors into practical use to achieve health benefits. The RPB proposed in this paper considers the absolute risk of a disease in intervention, and takes into account the risk-benefit effects simultaneously for a marker/exposure at the population level. The RPB illustrates a unique approach to quantitatively assess the risk and potential benefits of using a biomarker/risk factor for intervention in both early detection and prevention.