Cautioning the use of degree-day models for climate change projections in the presence of parametric uncertainty

Time lag effect on malaria transmission dynamics in an Amazonian Colombian municipality and importance for early warning systems

Malaria remains a significant public health problem worldwide, particularly in low-income regions with limited access to healthcare. Despite the use of antimalarial drugs, transmission remains an issue in Colombia, especially among indigenous populations in remote areas. In this study, we used an SIR Ross MacDonald model that considered land use change, temperature, and precipitation to analyze eco epidemiological parameters and the impact of time lags on malaria transmission in La Pedrera-Amazonas municipality. We found changes in land use between 2007 and 2020, with increases in forested areas, urban infrastructure and water edges resulting in a constant increase in mosquito carrying capacity. Temperature and precipitation variables exhibited a fluctuating pattern that corresponded to rainy and dry seasons, respectively and a marked influence of the El Niño climatic phenomenon. Our findings suggest that elevated precipitation and temperature increase malaria infection risk in the following 2 months. The risk is influenced by the secondary vegetation and urban infrastructure near primary forest formation or water body edges. These results may help public health officials and policymakers develop effective malaria control strategies by monitoring precipitation, temperature, and land use variables to flag high-risk areas and critical periods, considering the time lag effect.

Modelling of post-diapause development and spring emergence of Cydia nigricana (Lepidoptera: Tortricidae)

The prediction of the post-diapause emergence is the first step towards a comprehensive decision support system that can contribute to a considerable reduction of pesticide use by forecasting a precise spraying date. The cumulative field emergence can be described as a function of the cumulative development rate. We investigated the impact of seven constant temperatures and five light regimes on post-diapause development in laboratory experiments. Development rate depended significantly on temperature but not on photoperiod. We therefore fit non-linear thermal performance curves, a better and more modern approach over past linear models, to describe the development rate as a function of temperature. The four parameter Brière function was the most suitable and was subsequently applied to temperature data from 36 previous pea fields, where pea moth emergence was measured with pheromone traps in Northern Hesse (Germany). In order to describe the variation in development times between individuals, we fit five nonlinear distribution models to the cumulative development rate as a function of cumulative field emergence. The three parameter Gompertz model was selected as the best fitted model. We validated the model performance with an independent field data set. The model correctly predicted the first moth in the trap and the peak emergence in 81.82% of cases, with an average deviation of only 2.00 and 2.09 days respectively.

Computation of the conditions for anti-angiogenesis and gene therapy synergistic effects: Sensitivity analysis and robustness of target solutions

Both anti-angiogenesis and gene therapy involve complex processes depending on non-point parameters belonging to a space of values. To successfully overcome the challenges involved in their therapeutic approaches, there is a need to analyze the sensitivity of these parameters. In this paper, a new mathematical model that combines immune system stimulations, inflammatory processes associated with tumor development, and gene therapy aimed at enhancing the efficacy of both treatments are explored. Using the global sensitivity methods of Sobol and Morris, the most important parameters are estimated. Estimation of the sensitivity variance revealed a strong interdependence between the parameters. Also, determinations of the conditions for effective therapy lead to a target of reducing the cancer cell numbers by at least 50%. This opened the way for delimiting the parameter spaces making it possible to reach the treatment target in addition to enhancing the estimation of the minimum time of remission. The combination of therapies and sensitivity analysis have demonstrated the robustness of therapy success.

Two-Year Bee, or Not Two-Year Bee? How Voltinism Is Affected by Temperature and Season Length in a High-Elevation Solitary Bee

Organisms must often make developmental decisions without complete information about future conditions. This uncertainty-for example, about the duration of conditions favorable for growth-can favor bet-hedging strategies. Here, we investigated the causes of life cycle variation in Osmia iridis, a bee exhibiting a possible bet-hedging strategy with co-occurring 1- and 2-year life cycles. One-year bees reach adulthood quickly but die if they fail to complete pupation before winter; 2-year bees adopt a low-risk, low-reward strategy of postponing pupation until the second summer. We reared larval bees in incubators in various experimental conditions and found that warmer-but not longer-summers and early birthdates increased the frequency of 1-year life cycles. Using in situ temperature measurements and developmental trajectories of laboratory- and field-reared bees, we estimated degree-days required to reach adulthood in a single year. Local long-term (1950-2015) climate records reveal that this heat requirement is met in only ∼7% of summers, suggesting that the observed distribution of life cycles is adaptive. Warming summers will likely decrease average generation times in these populations. Nevertheless, survival of bees attempting 1-year life cycles-particularly those developing from late-laid eggs-will be <100%; consequently, we expect the life cycle polymorphism to persist.

Genetic Evidence of Contemporary Dispersal of the Intermediate Snail Host of Schistosoma japonicum: Movement of an NTD Host Is Facilitated by Land Use and Landscape Connectivity

BACKGROUND

While the dispersal of hosts and vectors-through active or passive movement-is known to facilitate the spread and re-emergence of certain infectious diseases, little is known about the movement ecology of Oncomelania spp., intermediate snail host of the parasite Schistosoma japonicum, and its consequences for the spread of schistosomiasis in East and Southeast Asia. In China, despite intense control programs aimed at preventing schistosomiasis transmission, there is evidence in recent years of re-emergence and persistence of infection in some areas, as well as an increase in the spatial extent of the snail host. A quantitative understanding of the dispersal characteristics of the intermediate host can provide new insights into the spatial dynamics of transmission, and can assist public health officials in limiting the geographic spread of infection.

METHODOLOGY/PRINCIPAL FINDINGS

Oncomelania hupensis robertsoni snails (n = 833) were sampled from 29 sites in Sichuan, China, genotyped, and analyzed using Bayesian assignment to estimate the rate of recent snail migration across sites. Landscape connectivity between each site pair was estimated using the geographic distance distributions derived from nine environmental models: Euclidean, topography, incline, wetness, land use, watershed, stream use, streams and channels, and stream velocity. Among sites, 14.4% to 32.8% of sampled snails were identified as recent migrants, with 20 sites comprising >20% migrants. Migration rates were generally low between sites, but at 8 sites, over 10% of the overall host population originated from one proximal site. Greater landscape connectivity was significantly associated with increased odds of migration, with the minimum path distance (as opposed to median or first quartile) emerging as the strongest predictor across all environmental models. Models accounting for land use explained the largest proportion of the variance in migration rates between sites. A greater number of irrigation channels leading into a site was associated with an increase in the site's propensity to both attract and retain snails.

CONCLUSIONS/SIGNIFICANCE

Our findings have important implications for controlling the geographic spread of schistosomiasis in China, through improved understanding of the dispersal capacity of the parasite's intermediate host.

Inferring pathological states in cortical neuron microcircuits

The brain activity is to a large extent determined by states of neural cortex microcircuits. Unfortunately, accuracy of results from neural circuits׳ mathematical models is often biased by the presence of uncertainties in underlying experimental data. Moreover, due to problems with uncertainties identification in a multidimensional parameters space, it is almost impossible to classify states of the neural cortex, which correspond to a particular set of the parameters. Here, we develop a complete methodology for determining uncertainties and the novel protocol for classifying all states in any neuroinformatic model. Further, we test this protocol on the mathematical, nonlinear model of such a microcircuit developed by Giugliano et al. (2008) and applied in the experimental data analysis of Huntington׳s disease. Up to now, the link between parameter domains in the mathematical model of Huntington׳s disease and the pathological states in cortical microcircuits has remained unclear. In this paper we precisely identify all the uncertainties, the most crucial input parameters and domains that drive the system into an unhealthy state. The scheme proposed here is general and can be easily applied to other mathematical models of biological phenomena.

Reliability of Degree-Day Models to Predict the Development Time of Plutella xylostella (L.) under Field Conditions

The diamondback moth, Plutella xylostella (L.), is a cosmopolitan pest of brassicaceous crops occurring in regions with highly distinct climate conditions. Several studies have investigated the relationship between temperature and P. xylostella development rate, providing degree-day models for populations from different geographical regions. However, there are no data available to date to demonstrate the suitability of such models to make reliable projections on the development time for this species in field conditions. In the present study, 19 models available in the literature were tested regarding their ability to accurately predict the development time of two cohorts of P. xylostella under field conditions. Only 11 out of the 19 models tested accurately predicted the development time for the first cohort of P. xylostella, but only seven for the second cohort. Five models correctly predicted the development time for both cohorts evaluated. Our data demonstrate that the accuracy of the models available for P. xylostella varies widely and therefore should be used with caution for pest management purposes.

Sporogonic Cycles Calculated Using Degree-Days, as a Basis for Comparison of Malaria Parasite Development in Different Eco-Epidemiological Settings in India

In India, malaria transmission is prevalent across diverse geologies and ecologies. Temperature is one of the key determinants of malarial transmission, causing low endemicity in some areas than in others. Using a degree-day model, we estimated the maximum and minimum possible number of days needed to complete a malarial sporogonic cycle (SC), in addition to the possible number of SCs for Plasmodium vivax and Plasmodium falciparum under two different ecological settings with either low or high endemicity for malaria at different elevations. In Raikhalkhatta (in the Himalayan foothills) SCs were modeled as not occurring from November to February, whereas in Gandhonia village (forested hills), all but only one month were suitable for malarial SCs. A minimum of 6 days and maximum of 46 days were required for completion of one SC. Forested hilly areas were more suitable for malaria parasite development in terms of SCs (25 versus 21 for P. falciparum and 32 versus 27 for P. vivax). Degree-days also provided a climatic explanation for the current transmission of malaria at different elevations. The calculation of degree-days and possible SC has applications in the regional analysis of transmission dynamics and management of malaria in view of climate change.

Developmental models for estimating ecological responses to environmental variability: structural, parametric, and experimental issues

Developmental models that account for the metabolic effect of temperature variability on poikilotherms, such as degree-day models, have been widely used to study organism emergence, range and development, particularly in agricultural and vector-borne disease contexts. Though simple and easy to use, structural and parametric issues can influence the outputs of such models, often substantially. Because the underlying assumptions and limitations of these models have rarely been considered, this paper reviews the structural, parametric, and experimental issues that arise when using degree-day models, including the implications of particular structural or parametric choices, as well as assumptions that underlie commonly used models. Linear and non-linear developmental functions are compared, as are common methods used to incorporate temperature thresholds and calculate daily degree-days. Substantial differences in predicted emergence time arose when using linear versus non-linear developmental functions to model the emergence time in a model organism. The optimal method for calculating degree-days depends upon where key temperature threshold parameters fall relative to the daily minimum and maximum temperatures, as well as the shape of the daily temperature curve. No method is shown to be universally superior, though one commonly used method, the daily average method, consistently provides accurate results. The sensitivity of model projections to these methodological issues highlights the need to make structural and parametric selections based on a careful consideration of the specific biological response of the organism under study, and the specific temperature conditions of the geographic regions of interest. When degree-day model limitations are considered and model assumptions met, the models can be a powerful tool for studying temperature-dependent development.

Challenges in predicting the effects of climate change on Schistosoma mansoni and Schistosoma haematobium transmission potential

Climate change will inevitably influence both the distribution of Schistosoma mansoni and Schistosoma haematobium and the incidence of schistosomiasis in areas where it is currently endemic, and impact on the feasibility of schistosomiasis control and elimination goals. There are several limitations of current models of climate and schistosome transmission, and substantial gaps in empirical data that impair model development. In this review we consider how temperature, precipitation, heat waves, drought, and flooding could impact on snail and schistosome population dynamics. We discuss how widely used degree day models of schistosome development may not be accurate at lower temperatures, and highlight the need for further research to improve our understanding of the relationship between air and water temperature and schistosome and snail development.

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors

Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis, the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001-2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057-2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses-including altered phenology-of disease vectors to altered climate.

Sensitivity analysis of infectious disease models: methods, advances and their application

Sensitivity analysis (SA) can aid in identifying influential model parameters and optimizing model structure, yet infectious disease modelling has yet to adopt advanced SA techniques that are capable of providing considerable insights over traditional methods. We investigate five global SA methods-scatter plots, the Morris and Sobol' methods, Latin hypercube sampling-partial rank correlation coefficient and the sensitivity heat map method-and detail their relative merits and pitfalls when applied to a microparasite (cholera) and macroparasite (schistosomaisis) transmission model. The methods investigated yielded similar results with respect to identifying influential parameters, but offered specific insights that vary by method. The classical methods differed in their ability to provide information on the quantitative relationship between parameters and model output, particularly over time. The heat map approach provides information about the group sensitivity of all model state variables, and the parameter sensitivity spectrum obtained using this method reveals the sensitivity of all state variables to each parameter over the course of the simulation period, especially valuable for expressing the dynamic sensitivity of a microparasite epidemic model to its parameters. A summary comparison is presented to aid infectious disease modellers in selecting appropriate methods, with the goal of improving model performance and design.