Dataset Information

Optimising predictive modelling of Ross River virus using meteorological variables.

ABSTRACT:

Background

Statistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia.

Methodology/principal findings

We developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model's ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance.

Conclusions/significance

We demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance.

SUBMITTER: Koolhof IS

PROVIDER: S-EPMC7978384 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. METHODS: We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. RESULTS: The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. CONCLUSIONS: This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.

Project description:Ross River virus (RRV) belongs to the genus Alphavirus and is prevalent in Australia. RRV infection can cause arthritic symptoms in patients and may include rash, fever, arthralgia, and myalgia. Type I interferons (IFN) are the primary antiviral cytokines and trigger activation of the host innate immune system to suppress the replication of invading viruses. Alphaviruses are able to subvert the type I IFN system, but the mechanisms used are ill defined. In this study, seven RRV field strains were analyzed for induction of and sensitivity to type I IFN. The sensitivities of these strains to human IFN-β varied significantly and were highest for the RRV 2548 strain. Compared to prototype laboratory strain RRV-T48, RRV 2548 also induced higher type I IFN levels both in vitro and in vivo and caused milder disease. To identify the determinants involved in type I IFN modulation, the region encoding the nonstructural proteins (nsPs) of RRV 2548 was sequenced, and 42 amino acid differences from RRV-T48 were identified. Using fragment swapping and site-directed mutagenesis, we discovered that substitutions E402A and R522Q in nsP1 as well as Q619R in nsP2 were responsible for increased sensitivity of RRV 2548 to type I IFN. In contrast, substitutions A31T, N219T, S580L, and Q619R in nsP2 led to induction of higher levels of type I IFN. With exception of E402A, all these variations are common for naturally occurring RRV strains. However, they are different from all known determinants of type I IFN modulation reported previously in nsPs of alphaviruses.IMPORTANCE By identifying natural Ross River virus (RRV) amino acid determinants for type I interferon (IFN) modulation, this study gives further insight into the mechanism of type I IFN modulation by alphaviruses. Here, the crucial role of type I IFN in the early stages of RRV disease pathogenesis is further demonstrated. This study also provides a comparison of the roles of different parts of the RRV nonstructural region in type I IFN modulation, highlighting the importance of nonstructural protein 1 (nsP1) and nsP2 in this process. Three substitutions in nsP1 and nsP2 were found to be independently associated with enhanced type I IFN sensitivity, and four independent substitutions in nsP2 were important in elevated type I IFN induction. Such evidence has clear implications for RRV immunobiology, persistence, and pathology. The identification of viral proteins that modulate type I IFN may also have importance for the pathogenesis of other alphaviruses.