Search

Epicentres are the focus of COVID‐19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi‐regional, hierarchical‐tier mathematical model for better understanding the complexity and heterogeneity of COVID‐19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi‐regional, hierarchical‐tier SLIHR model. With this model, we revealed insight into how COVID‐19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi‐regional, hierarchical‐tier SLIHR model revealed insight into how COVID‐19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID‐19 pandemic.

Background To reduce the coronavirus disease burden in England, along with many other countries, the government implemented a package of non-pharmaceutical interventions (NPIs) that have also impacted other transmissible infectious diseases such as norovirus. It is unclear what future norovirus disease incidence is likely to look like upon lifting these restrictions. Methods Here we use a mathematical model of norovirus fitted to community incidence data in England to project forward expected incidence based on contact surveys that have been collected throughout 2020–2021. Results We report that susceptibility to norovirus infection has likely increased between March 2020 and mid-2021. Depending upon assumptions of future contact patterns incidence of norovirus that is similar to pre-pandemic levels or an increase beyond what has been previously reported is likely to occur once restrictions are lifted. Should adult contact patterns return to 80% of pre-pandemic levels, the incidence of norovirus will be similar to previous years. If contact patterns return to pre-pandemic levels, there is a potential for the expected annual incidence to be up to 2-fold larger than in a typical year. The age-specific incidence is similar across all ages. Conclusions Continued national surveillance for endemic diseases such as norovirus will be essential after NPIs are lifted to allow healthcare services to adequately prepare for a potential increase in cases and hospital pressures beyond what is typically experienced.

Background SARS-CoV-2 dynamics are driven by human behaviour. Social contact data are of utmost importance in the context of transmission models of close-contact infections. Methods Using online representative panels of adults reporting on their own behaviour as well as parents reporting on the behaviour of one of their children, we collect contact mixing (CoMix) behaviour in various phases of the COVID-19 pandemic in over 20 European countries. We provide these timely, repeated observations using an online platform: SOCRATES-CoMix. In addition to providing cleaned datasets to researchers, the platform allows users to extract contact matrices that can be stratified by age, type of day, intensity of the contact and gender. These observations provide insights on the relative impact of recommended or imposed social distance measures on contacts and can inform mathematical models on epidemic spread. Conclusion These data provide essential information for policymakers to balance non-pharmaceutical interventions, economic activity, mental health and wellbeing, during vaccine rollout.

Tijuana, Mexico, has a concentrated HIV epidemic among overlapping key populations (KPs) including people who inject drugs (PWID), female sex workers (FSW), their male clients, and men who have sex with men (MSM). We developed a dynamic HIV transmission model among these KPs to determine the extent to which their unmet prevention and treatment needs is driving HIV transmission. Over 2020-2029 we estimated the proportion of new infections acquired in each KP, and the proportion due to their unprotected risk behaviours. We estimate that 43.7% and 55.3% of new infections are among MSM and PWID, respectively, with FSW and their clients making-up 10% of new infections. Projections suggest 93.8% of new infections over 2020-2029 will be due to unprotected sex between MSM or unsafe injecting drug use. Prioritizing interventions addressing sexual and injecting risks among MSM and PWID are critical to controlling HIV in Tijuana.Tijuana, México, tiene una epidemia de VIH concentrada en poblaciones claves (PC) superpuestas que incluyen personas que se inyectan drogas (PID), trabajadoras sexuales (MTS), sus clientes hombres, y hombres que tienen sexo con hombres (HSH). Desarrollamos un modelo dinámico de transmisión de VIH en estas PC para determinar hasta dónde sus necesidades no atendidas de prevención y tratamiento dirigen la transmisión del VIH. Para 2020–2029 estimamos la proporción de nuevas infecciones adquiridas en cada PC, y la proporción atribuida a sus comportamientos de riesgo sin protección. Estimamos que 43.7% y 55.3% de nuevas infecciones se dan en HSH y PID, respectivamente, con MTS y clientes conformando 10% de nuevas infecciones. Las proyecciones sugieren que 93.8% de nuevas infecciones en 2020–2029 se deberán a sexo sin protección en HSH o uso inseguro de drogas inyectables. Dar prioridad a intervenciones que atienden los riesgos sexual y de inyección en HSH y PID es crítico para controlar el VIH en Tijuana.

Background Due to spatial heterogeneity in onchocerciasis transmission, the duration of ivermectin mass drug administration (MDA) required for eliminating onchocerciasis will vary within endemic areas and the occurrence of transmission “hotspots” is inevitable. The geographical scale at which stop-MDA decisions are made will be a key driver in how rapidly national programs can scale down active intervention upon achieving the epidemiological targets for elimination. Methods We used 2 onchocerciasis models (EPIONCHO-IBM and ONCHOSIM) to predict the likelihood of achieving elimination by 2030 in Africa, accounting for variation in preintervention endemicity levels and histories of ivermectin treatment. We explore how decision making at contrasting geographical scales (community vs larger scale “project”) changes projections on populations still requiring MDA or transitioning to post-treatment surveillance. Results The total population considered grows from 118 million people in 2020 to 136 million in 2030. If stop-MDA decisions are made at project level, the number of people requiring treatment declines from 69–118 million in 2020 to 59–118 million in 2030. If stop-MDA decisions are made at community level, the numbers decline from 23–81 million in 2020 to 15–63 million in 2030. The lower estimates in these prediction intervals are based on ONCHOSIM, the upper limits on EPIONCHO-IBM. Conclusions The geographical scale at which stop-MDA decisions are made strongly determines how rapidly national onchocerciasis programs can scale down MDA programs. Stopping in portions of project areas or transmission zones would free up human and economic resources.

Colon cancer is the third most commonly diagnosed cancer and the second leading cause of cancer death in men and women combined in the United States. In this work, we performed mathematical and statistical modelling of Tumour sizes as a function of age for four different races. Mathematically, based on the behaviour of the data for each race, we partitioned ages of subjects into several intervals. The mathematical function that characterizes the size of the Tumour as a function of age was determined for each age interval. Statistically, using quantile regression, we designed models that are more robust at specific quantiles using Tumour size and age as dependent and predictor variables.

Individual differences in semantic categorization are commonplace. Individuals apply a word like SPORTS to different instances because they employ different conditions for category membership (vagueness in criteria) or because they differ regarding the extent to which they feel the term can be applied given fixed conditions (vagueness in degree). Three individuals may, for instance, disagree as to whether chess and hiking are SPORTS, because one believes SPORTS are competitive in nature, while the other two require SPORTS to be effortful (vagueness in criteria). On the basis of whether they consider hiking sufficiently effortful or not, the latter two individuals might still disagree as to whether to call it a SPORT (vagueness in degree). We investigated whether there are systematic age-related differences in semantic categorization by analyzing the categorization decisions of 1,868 adults for eight semantic categories with a formal model that allows the two sources of categorization differences to be disentangled. We found that young and older adults assess instances differently with respect to the categorization conditions and that older adults employ a lower threshold for category membership than young adults do. We recommend that these criteria and degree differences are taken into account in studies of age-related semantic processing. ispartof: Journal of Cognition vol:2 issue:1 pages:1-20 ispartof: location:England status: published

We report on the dynamic scaling of the diffusion growth phase of the COVID-19 epidemic in Europe. During this initial diffusion stage, the European countries implemented unprecedented mitigation polices to delay and suppress the disease contagion, although not in a uniform way or timing. Despite this diversity, we find that the reported fatality cases grow following a power law in all European countries we studied. The difference among countries is the value of the power-law exponent 3.5

The outbreak of coronavirus COVID-19 is spreading at an unprecedented rate to the human populations and taking several thousands of life all over the world. Scientists are trying to map the pattern of the transmission of coronavirus (SARS-CoV-2). Many countries are in the phase of lockdown in the globe. In this paper we predict about the effect of coronavirus COVID-19 and give a sneak peak when it will reduce the transmission rate in the world via mathematical modelling. In this research work our study is based on extensions of the well-known susceptible-exposed-infected-recovered (SEIR) family of compartmental models and later we observe the new model changes into (SEIR) without changing its physical meanings. The stability analysis of the coronavirus depends on changing of its basic reproductive ratio. The progress rate of the virus in the critically infected cases and the recovery rate have major roles to control this epidemic. The impact of social distancing, lockdown of the country, self-isolation, home quarantine and the wariness of global public health system have significant influence on the parameters of the model system that can alter the effect of recovery rates, mortality rates and active contaminated cases with the progression of time in the real world. The prognostic ability of mathematical model is circumscribed as of the accuracy of the available data and its application to the problem.

Highlights • Mixtures of 29 halogenated POPs were reconstructed based on human blood levels. • In vitro assays permitted to evaluate DNT effects in human iPSC-derived NSCs. • POPs at human blood levels increased proliferating NSCs and decreased synapses. • Mathematical modelling showed that synaptogenesis was the most sensitive DNT endpoint. • Data support a possible link between POPs and neurodevelopmental disorders., Halogenated persistent organic pollutants (POPs) like perfluorinated alkylated substances (PFASs), brominated flame retardants (BFRs), organochlorine pesticides and polychlorinated biphenyls (PCBs) are known to cause cancer, immunotoxicity, neurotoxicity and interfere with reproduction and development. Concerns have been raised about the impact of POPs upon brain development and possibly neurodevelopmental disorders. The developing brain is a particularly vulnerable organ due to dynamic and complex neurodevelopmental processes occurring early in life. However, very few studies have reported on the effects of POP mixtures at human relevant exposures, and their impact on key neurodevelopmental processes using human in vitro test systems. Aiming to reduce this knowledge gap, we exposed mixed neuronal/glial cultures differentiated from neural stem cells (NSCs) derived from human induced pluripotent stem cells (hiPSCs) to reconstructed mixtures of 29 different POPs using concentrations comparable to Scandinavian human blood levels. Effects of the POP mixtures on neuronal proliferation, differentiation and synaptogenesis were evaluated using in vitro assays anchored to common key events identified in the existing developmental neurotoxicity (DNT) adverse outcome pathways (AOPs). The present study showed that mixtures of POPs (in particular brominated and chlorinated compounds) at human relevant concentrations increased proliferation of NSCs and decreased synapse number. Based on a mathematical modelling, synaptogenesis and neurite outgrowth seem to be the most sensitive DNT in vitro endpoints. Our results indicate that prenatal exposure to POPs may affect human brain development, potentially contributing to recently observed learning and memory deficits in children.

The Polycomb system is essential for stable gene silencing in many organisms. This regulation is achieved in part through addition of the histone modifications H3K27me2/me3 by Polycomb Repressive Complex 2 (PRC2). These modifications are believed to be the causative epigenetic memory elements of PRC2-mediated silencing. As these marks are stored locally in the chromatin, PRC2-based memory is a cis-acting system. A key feature of stable epigenetic memory in cis is PRC2-mediated, self-reinforcing feedback from K27-methylated histones onto nearby histones in a read-write paradigm. However, it was not clear under what conditions such feedback can lead to stable memory, able, for example, to survive the perturbation of histone dilution at DNA replication. In this context, computational modelling has allowed a rigorous exploration of possible underlying memory mechanisms and has also greatly accelerated our understanding of switching between active and silenced states. Specifically, modelling has predicted that switching and memory at Polycomb loci is digital, with a locus being either active or inactive, rather than possessing intermediate, smoothly varying levels of activation. Here, we review recent advances in models of Polycomb control, focusing on models of epigenetic switching through nucleation and spreading of H3K27me2/me3. We also examine models that incorporate transcriptional feedback antagonism and those including bivalent chromatin states. With more quantitative experimental data on histone modification kinetics, as well as single-cell resolution data on transcription and protein levels for PRC2 targets, we anticipate an expanded need for modelling to help dissect increasingly interconnected and complex memory mechanisms.

BackgroundThe development and widespread use of an effective SARS-CoV-2 vaccine could help prevent substantial morbidity and mortality associated with COVID-19 infection and mitigate many of the secondary effects associated with non-pharmaceutical interventions. The limited availability of an effective and licensed vaccine will task policymakers around the world, including in India, with decisions regarding optimal vaccine allocation strategies. Using mathematical modelling we aimed to assess the impact of different age-specific COVID-19 vaccine allocation strategies within India on SARS CoV-2-related mortality and infection.MethodsWe used an age-structured, expanded SEIR model with social contact matrices to assess different age-specific vaccine allocation strategies in India. We used state-specific age structures and disease transmission coefficients estimated from confirmed Indian incident cases of COVID-19 between 28 January and 31 August 2020. Simulations were used to investigate the relative reduction in mortality and morbidity of vaccinate allocation strategies based on prioritizing different age groups, and the interactions of these strategies with several concurrent non-pharmacologic interventions (i.e., social distancing, mandated masks, lockdowns). Given the uncertainty associated with current COVID-19 vaccine development, we also varied several vaccine characteristics (i.e., efficacy, type of immunity conferred, and rollout speed) in the modelling simulations.ResultsIn nearly all scenarios, prioritizing COVID-19 vaccine allocation for older populations (i.e., >60yrs old) led to the greatest relative reduction in deaths, regardless of vaccine efficacy, control measures, rollout speed, or immunity dynamics. However, preferential vaccination of this target group often produced higher total symptomatic infection counts and more pronounced estimates of peak incidence than strategies which targeted younger adults (i.e., 20-40yrs or 40-60yrs) or the general population irrespective of age. Vaccine efficacy, immunity type, target coverage and rollout speed all significantly influenced overall strategy effectiveness, with the time taken to reach target coverage significantly affecting the relative mortality benefit comparative to no vaccination.ConclusionsOur findings support global recommendations to prioritize COVID-19 vaccine allocation for older age groups. Including younger adults in the prioritisation group can reduce overall infection rates, although this benefit was countered by the larger mortality rates in older populations. Ultimately an optimal vaccine allocation strategy will depend on vaccine characteristics, strength of concurrent non-pharmaceutical interventions, and region-specific goals such as reducing mortality, morbidity, or peak incidence.

International audience; In modern cropping systems, the near-universal use of plant protection products selects for resistance in pest populations. The emergence and evolution of this adaptive trait threaten treatment efficacy. We identified determinants of fungicide resistance evolution and quantified their effects at a large spatiotemporal scale. We focused on Zymoseptoria tritici, which causes leaf blotch in wheat. Phenotypes of qualitative or quantitative resistance to various fungicides were monitored annually, from 2004 to 2017, at about 70 sites throughout 22 regions of France (territorial units of 25000km2 on average). We modelled changes in resistance frequency with regional anti-Septoria fungicide use, yield losses due to the disease and the regional area under organic wheat. The major driver of resistance dynamics was fungicide use at the regional scale. We estimated its effect on the increase in resistance and relative apparent fitness of each resistance phenotype. The predictions of the model replicated the spatiotemporal patterns of resistance observed in field populations (R2 from 0.56 to 0.82). The evolution of fungicide resistance is mainly determined at the regional scale. This study therefore showed that collective management at the regional scale could effectively complete local actions.This article is protected by copyright. All rights reserved.

Background: The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centrepiece in evidence-based disease management. Numerous approaches that use mathematical modelling have been used to predict the outcome of the pandemic, including data-driven models, empirical and hybrid models. This study was aimed at prediction of COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Material and Methods: Real-time Indian data of cumulative cases and deaths of COVID-19 was retrieved from the Johns Hopkins dashboard. The dataset from 11 March 2020 to 25 June 2020 (n = 107 time points) was used to fit the autoregressive integrated moving average model. The model with minimum Akaike Information Criteria was used for forecasting. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) were used to validate the model. Results: The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths, respectively, with minimum Akaike Information Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 June 2020 to 5 July 2020 showed a trend toward continuous increment. The PredRMSE and BaseRMSE of ARIMA (1,3,2) model were 21,137 and 166,330, respectively. Similarly, PredRMSE and BaseRMSE of ARIMA (3,3,1) model were 668.7 and 5,431, respectively. Conclusion: It is proposed that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimisation and evidence-based decision making for a subsequent state of affairs.

Genome-scale mechanistic models of pathways are gaining importance for genomic data interpretation because they provide a natural link between genotype measurements (transcriptomics or genomics data) and the phenotype of the cell (its functional behavior). Moreover, mechanistic models can be used to predict the potential effect of interventions, including drug inhibitions. Here, we present the implementation of a mechanistic model of cell signaling for the interpretation of transcriptomic data as an R/Bioconductor package, a Cytoscape plugin and a web tool with enhanced functionality which includes building interpretable predictors, estimation of the effect of perturbations and assessment of the effect of mutations in complex scenarios., This work is supported by grants SAF2017-88908-R from the Spanish Ministry of Economy and Competitiveness and PT17/0009/0006 and PI20/01305 from the ISCIII, both co-funded with European Regional Development Funds (ERDF) as well as H2020 Programme of the European Union grants Marie Curie Innovative Training Network “Machine Learning Frontiers in Precision Medicine” (MLFPM) (GA 813533) and “ELIXIR-EXCELERATE fast-track ELIXIR implementation and drive early user exploitation across the life sciences” (GA 676559) to JD. Funding for open access charge: from the Spanish Ministry of Economy and Competitiveness / SAF2017-88908-R.

Background Zika virus (ZIKV) emerged as a global epidemic in 2015–2016 from Latin America with its true geographical extent remaining unclear due to widely presumed underreporting. The identification of locations with potential and unknown spread of ZIKV is a key yet understudied component for outbreak preparedness. Here, we aim to identify locations at a high risk of cryptic ZIKV spread during 2015–2016 to further the understanding of the global ZIKV epidemiology, which is critical for the mitigation of the risk of future epidemics. Methods We developed an importation simulation model to estimate the weekly number of ZIKV infections imported in each susceptible spatial unit (i.e. location that did not report any autochthonous Zika cases during 2015–2016), integrating epidemiological, demographic, and travel data as model inputs. Thereafter, a global risk model was applied to estimate the weekly ZIKV transmissibility during 2015–2016 for each location. Finally, we assessed the risk of onward ZIKV spread following importation in each susceptible spatial unit to identify locations with a high potential for cryptic ZIKV spread during 2015–2016. Results We have found 24 susceptible spatial units that were likely to have experienced cryptic ZIKV spread during 2015–2016, of which 10 continue to have a high risk estimate within a highly conservative scenario, namely, Luanda in Angola, Banten in Indonesia, Maharashtra in India, Lagos in Nigeria, Taiwan and Guangdong in China, Dakar in Senegal, Maputo in Mozambique, Kinshasa in Congo DRC, and Pool in Congo. Notably, among the 24 susceptible spatial units identified, some have reported their first ZIKV outbreaks since 2017, thus adding to the credibility of our results (derived using 2015–2016 data only). Conclusion Our study has provided valuable insights into the potentially high-risk locations for cryptic ZIKV circulation during the 2015–2016 pandemic and has also laid a foundation for future studies that attempt to further narrow this key knowledge gap. Our modelling framework can be adapted to identify areas with likely unknown spread of other emerging vector-borne diseases, which has important implications for public health readiness especially in resource-limited settings.

Objectives The aim of the study was to assess the impact of social distancing interventions in Greece and to examine what would have happened if those interventions had not been implemented. Study design A dynamic, discrete time, stochastic individual-based model was developed to simulate coronavirus disease 2019 (COVID-19) transmission. Methods The model was fitted to the observed trends in COVID-19 deaths and intensive care unit (ICU) bed use in Greece. Results If Greece had not implemented social distancing interventions, the healthcare system would have been overwhelmed between March 30 and April 4. The combined social distancing interventions and increase in ICU beds averted 4360 (95% credible interval: 3050, 5700) deaths and prevented the healthcare system from becoming overwhelmed. Conclusions The quick and accurate interventions of the Greek government limited the burden of the COVID-19 outbreak., Highlights • Without the social distancing (SD) interventions, the healthcare system would have been overwhelmed by early April. • The early implementation of the SD interventions prevented 4360 (95% credible interval: 3050, 5700) deaths by April 27. • Any interventions to boost health care capacity, without the simultaneous of SD interventions, would not have been effective. • The decision of the Greek government to launch early SD measures resulted in limiting the burden of the COVID-19 outbreak.

Computational and mathematical modelling towards understanding the structure and dynamics of biological systems has significantly impacted on translational neuroscience to face novel approaches toward neurological disorders such as Alzheimer’s (AD) and Parkinson’s disease (PD). In this study, a computational model of AD and PD have been modelled using biochemical systems theory, and shows how Tumour Necrosis Factor alpha (TNF훼) regulated neuroinflammation, oxidative stress and insulin pathways can dysregulate its downstream signalling cascade that lead to neurodegeneration observed in AD and PD. The experimental data for initial conditions for this model and validation of the model was based on data reported in literature. In simulations, elevations in the aggregations of major proteins involved in the pathology of AD and PD including amyloid beta, alpha synuclein, tau have been modelled. Abnormal aggregation of these proteins and hyperphosphorylation of tau were observed in the model. This aggregation may lead to developing Lewy bodies, fibrils, plaques and tangles inside neurons that trigger apoptosis. An increase in the concentrations of TNF훼 and glutamate during diseased conditions was noted in the model. Accumulation of these proteins may be related to the feedback mechanism of TNF훼 that initiates its own release and the production of excess glutamate. This could lead to the prolonged activation of microglia that result in death of surrounding neurons. With the elevation in reactive oxygen species, oxidative stress also increased. Simulations suggest insulin may be an important factor identifying neurodegeneration in AD and PD, through its action along with the neuroinflammation and oxidative stress. Low insulin level was noticed in the diseased condition due to abnormal protein aggregation that leads to TNFα release. Given the role towards better design of real experiments, accumulation of oligomers of mutated proteins in AD and PD activating microglia and secreting TNFα along with other cytokines map to oxidative stress that led to cell death.

Acidosis of the tumor microenvironment leads to cancer invasion, progression and resistance to therapies. We present a biophysical model that describes how tumor cells regulate intracellular and extracellular acidity while they grow in a microenvironment characterized by increasing acidity and hypoxia. The model takes into account the dynamic interplay between glucose and $$\hbox {O}_2$$ O 2 consumption with lactate and $$\hbox {CO}_2$$ CO 2 production and connects these processes to $$\hbox {H}^+$$ H + and $$\hbox {HCO}_3^-$$ HCO 3 - fluxes inside and outside cells. We have validated the model with independent experimental data and used it to investigate how and to which extent tumor cells can survive in adverse micro-environments characterized by acidity and hypoxia. The simulations show a dominance of the $$\hbox {H}^+$$ H + exchanges in well-oxygenated regions, and of $$\hbox {HCO}_3^-$$ HCO 3 - exchanges in the inner hypoxic regions where tumor cells are known to acquire malignant phenotypes. The model also includes the activity of the enzyme Carbonic Anhydrase 9 (CA9), a known marker of tumor aggressiveness, and the simulations demonstrate that CA9 acts as a nonlinear $$\hbox {pH}_i$$ pH i equalizer at any $$\hbox {O}_2$$ O 2 level in cells that grow in acidic extracellular environments.

BackgroundThe emergence of a novel coronavirus (SARS-CoV-2) in Wuhan, China, at the end of 2019 has caused widespread transmission around the world. As new epicentres in Europe and America have arisen, of particular concern is the increased number of imported coronavirus disease 2019 (COVID-19) cases in Africa, where the impact of the pandemic could be more severe. We aim to estimate the number of COVID-19 cases imported from 12 major epicentres in Europe and America to each African country, as well as the probability of reaching 10,000 infections in total by the end of March, April, and May following viral introduction.MethodsWe used the reported number of cases imported from the 12 major epicentres in Europe and America to Singapore, as well as flight data, to estimate the number of imported cases in each African country. Under the assumption that Singapore has detected all the imported cases, the estimates for Africa were thus conservative. We then propagated the uncertainty in the imported case count estimates to simulate the onward spread of the virus, until 10,000 infections are reached or the end of May, whichever is earlier. Specifically, 1,000 simulations were run separately under two scenarios, where the reproduction number under the stay-at-home order was assumed to be 1.5 and 1.0 respectively.FindingsWe estimated Morocco, Algeria, South Africa, Egypt, Tunisia, and Nigeria as having the largest number of COVID-19 cases imported from the 12 major epicentres. Based on our 1,000 simulation runs, Morocco and Algeria’s estimated probability of reaching 10,000 infections by end of March was close to 100% under both scenarios. In particular, we identified countries with less than 100 cases in total reported by end of April whilst the estimated probability of reaching 10,000 infections by then was higher than 50% even under the more optimistic scenario.ConclusionOur study highlights particular countries that are likely to reach (or have reached) 10,000 infections far earlier than the reported data suggest, calling for the prioritization of resources to mitigate the further spread of the epidemic.

Objective To derive and validate a mathematical model to predict laser-induced temperature changes in a kidney during kidney stone treatment. Methods A simplified mathematical model to predict temperature change in the kidney for any given renal volume, irrigation flow rate, irrigation fluid temperature, and laser power was derived. We validated our model with matched in vitro experiments. Results Excellent agreement between the mathematical model predictions and laboratory data was obtained. Conclusion The model obviates the need for repeated experimental validation. The model predicts scenarios where risk of renal tissue damage is high. With real-time knowledge of flow rate, irrigating fluid temperature and laser usage, safety warning levels could be predicted. Meanwhile, clinicians should be aware of the potential risk from thermal injury and take measures to reduce the risk, such as using room temperature irrigation fluid and judicious laser use.

Background Seasonal influenza poses a significant public health and economic burden, associated with the outcome of infection and resulting complications. The true burden of the disease is difficult to capture due to the wide range of presentation, from asymptomatic cases to non-respiratory complications such as cardiovascular events, and its seasonal variability. An understanding of the magnitude of the true annual incidence of influenza is important to support prevention and control policy development and to evaluate the impact of preventative measures such as vaccination. Methods We use a dynamic disease transmission model, laboratory-confirmed influenza surveillance data, and randomized-controlled trial (RCT) data to quantify the underestimation factor, expansion factor, and symptomatic influenza illnesses in the US and Canada during the 2011-2012 and 2012-2013 influenza seasons. Results Based on 2 case definitions, we estimate between 0.42−3.2% and 0.33−1.2% of symptomatic influenza illnesses were laboratory-confirmed in Canada during the 2011-2012 and 2012-2013 seasons, respectively. In the US, we estimate between 0.08−0.61% and 0.07−0.33% of symptomatic influenza illnesses were laboratory-confirmed in the 2011-2012 and 2012-2013 seasons, respectively. We estimated the symptomatic influenza illnesses in Canada to be 0.32−2.4 million in 2011-2012 and 1.8−8.2 million in 2012-2013. In the US, we estimate the number of symptomatic influenza illnesses to be 4.4−34 million in 2011-2012 and 23−102 million in 2012-2013. Conclusions We illustrate that monitoring a representative group within a population may aid in effectively modelling the transmission of infectious diseases such as influenza. In particular, the utilization of RCTs in models may enhance the accuracy of epidemiological parameter estimation.

Plant membrane transport, like transport across all eukaryotic membranes, is highly non-linear and leads to interactions with characteristics so complex that they defy intuitive understanding. The physiological behaviour of stomatal guard cells is a case in point in which, for example, mutations expected to influence stomatal closing have profound effects on stomatal opening and manipulating transport across the vacuolar membrane affects the plasma membrane. Quantitative mathematical modelling is an essential tool in these circumstances, both to integrate the knowledge of each transport process and to understand the consequences of their manipulation in vivo. Here, we outline the OnGuard modelling environment and its use as a guide to predicting the emergent properties arising from the interactions between non-linear transport processes. We summarise some of the recent insights arising from OnGuard, demonstrate its utility in interpreting stomatal behaviour, and suggest ways in which the OnGuard environment may facilitate ‘reverse-engineering’ of stomata to improve water use efficiency and carbon assimilation.

The article discusses the forecast of the development of the coronavirus disease (COVID-19) on the basis of the SIR (Susceptible, Infected, Recovered) type model and official morbidity statistics.

Purpose of this study was to develop the main components of a model of an adaptive information system for predicting crop productivity. Methods. To conduct research on the establishment of the basic structural elements of an adaptive information model for predicting the productivity of basic crops used the method of constructing dynamic models. Results. A detailed analysis of conceptual approaches to the construction of mathematical agricultural models is carried out and the main advantages and disadvantages of modern analogues are established. It is determined that the adaptive information model is based solely on the needs of the plant and actually on the need to provide these needs with available resources in order to obtain consistently high yields with high quality indicators. The hardware and software complex must have a feedback relationship between its basic structural elements, because it significantly improves the accuracy of predicting plant productivity. Data based on the operation of certain mechanisms or indicators of weather conditions and their forecasts are used for decision making, however, if they are substantially changed, decisions about individual technology elements are reviewed. The software should be related to the economic part and should take into account market conditions and forecast data when making recommendations. In the case of low purchase prices for products, we recommend that certain agrotechnical operations (say vegetation feeding) be applied or not, in the case of significant change in growing conditions - when the application of these agro-measures will be ineffective due to the negative effects of drought, etc. Conclusions. Adaptive information system for forecasting productivity in the technological process of growing crops is formed on the basis of a model consisting of three modules of characteristics – the resultant and two components. At each subsequent stage of implementation of the model, the resulting feature becomes component, with the maximum contribution to the resulting feature of the next module.

Objective::Prices of antiretroviral (ARV) drugs in lower income countries have decreased substantially over the past two decades, helping to facilitate greatly expanded access to antiretroviral therapy (ART). However, ART coverage in many parts of the world remains low. We investigate the extent of epidemiological benefits that might be expected if ARV drug prices decline further.Design:A modeling study using data from seven countries in West and Central Africa (Cameroon, Democratic Republic of the Congo, Côte d'Ivoire, Niger, Nigeria, Senegal, and Togo).Methods:We investigated how the timing of ARV cost reductions could affect the impact and compared three different possible investment strategies: reinvesting in ART, reinvesting in the HIV response according to historical allocations, and reinvesting with the aim of minimizing HIV incidence and mortality.Results:If ARV drug prices fell by 37% relative to 2018 levels (i.e. following continued trend declines), we calculate ART unit costs could decrease by ∼20% (holding other cost components constant). If this could be achieved by 2020 and the savings were reinvested into ART, we estimate that an additional 8% of HIV infections and 11% of HIV-related deaths could be averted over 2020-2030 across the seven countries. Slightly greater gains could be attained if funds were reinvested in ART in combination with primary prevention. Delays in the year of introduction of ARV price reductions would reduce the impact by about 1% per year.Conclusion:ARV price reductions could free up funds that - if strategically invested - would help countries move closer toward the elimination of HIV.

Background More than ever, it is crucial to make the best use of existing country data, and analytical tools for developing malaria control strategies as the heterogeneity in malaria risk within countries is increasing, and the available malaria control tools are expanding while large funding gaps exist. Global and local policymakers, as well as funders, increasingly recognize the value of mathematical modelling as a strategic tool to support decision making. This case study article describes the long-term use of modelling in close collaboration with the National Malaria Control Programme (NMCP) in Tanzania, the challenges encountered and lessons learned. Case description In Tanzania, a recent rebound in prevalence led to the revision of the national malaria strategic plan with interventions targeted to the malaria risk at the sub-regional level. As part of the revision, a mathematical malaria modelling framework for setting specific predictions was developed and used between 2016 and 2019 to (1) reproduce setting specific historical malaria trends, and (2) to simulate in silico the impact of future interventions. Throughout the project, multiple stakeholder workshops were attended and the use of mathematical modelling interactively discussed. Evaluation In Tanzania, the model application created an interdisciplinary and multisectoral dialogue platform between modellers, NMCP and partners and contributed to the revision of the national malaria strategic plan by simulating strategies suggested by the NMCP. The uptake of the modelling outputs and sustained interest by the NMCP were critically associated with following factors: (1) effective sensitization to the NMCP, (2) regular and intense communication, (3) invitation for the modellers to participate in the strategic plan process, and (4) model application tailored to the local context. Conclusion Empirical data analysis and its use for strategic thinking remain the cornerstone for evidence-based decision-making. Mathematical impact modelling can support the process both by unifying all stakeholders in one strategic process and by adding new key evidence required for optimized decision-making. However, without a long-standing partnership, it will be much more challenging to sensibilize programmes to the usefulness and sustained use of modelling and local resources within the programme or collaborating research institutions need to be mobilized.

The aim of this study is to investigate how an augmented reality mobile math trails program can provide opportunities for students to engage in meaningful mathematical modelling activities. An explorative research design was conducted involving two mathematics teachers and 30 eight grades in Semarang, Indonesia. An Augmented Reality Mobile Math Trails App was created, and several math trail tasks were designed, then students run the activity. Data were gathered by means of participatory observation, interviews, questionnaires, tests, and worksheets. Data analysis began with the organisation, annotation, description of the data and statistic tests. The findings indicate that an educational program was successfully designed, which offered students a meaningful mathematical experience. A mobile app was also developed to support this program. The mobile app with augmented reality features is helpful for students as a tool that bridges the gap between real-world situations and mathematical concepts in problem-solving following the mathematical modelling cycle. The program thus contributes to a higher ability in mathematical modelling. The study identified a link between instrumented techniques in programs and mathematical modelling, as built during the instrumentation process. Further studies are essential for project development and implementation in other cities with different situations and aspects of study.

The bacterial production of acetate via reductive acetogenesis along the Wood–Ljungdahl metabolic pathway is an important source of this molecule in several environments, ranging from industrial bioreactors to the human gastrointestinal tract. Here, we contributed to the study of reductive acetogens by considering mathematical modelling techniques for the prediction of bacterial growth and acetate production. We found that the incorporation of a hydrogen uptake concentration threshold into the models improves their predictions and we calculated this threshold as 86.2 mM (95% confidence interval 6.1–132.6 mM). Monod kinetics and first-order kinetics models, with the inclusion of two candidate threshold terms or reversible Michaelis–Menten kinetics, were compared to experimental data and the optimal formulation for predicting both growth and metabolism was found. The models were then used to compare the efficacy of two growth media for acetogens. We found that the recently described general acetogen medium was superior to the DSMZ medium in terms of unbiased estimation of acetogen growth and investigated the contribution of yeast extract concentration to acetate production and bacterial growth in culture. The models and their predictions will be useful to those studying both industrially and environmentally relevant reductive acetogenesis and allow for straightforward adaptation to similar cases with different organisms.

Background Nutrition plays a crucial role in regulating reproductive hormones and follicular development in cattle. This is visible particularly during the time of negative energy balance at the onset of milk production after calving. Here, elongated periods of anovulation have been observed, resulting from alterations in luteinizing hormone concentrations, likely caused by lower glucose and insulin concentrations in the blood. The mechanisms that result in a reduced fertility are not completely understood, although a close relationship to the glucose-insulin metabolism is widely supported. Results Following this idea, we developed a mathematical model of the hormonal network combining reproductive hormones and hormones that are coupled to the glucose compartments within the body of the cow. The model is built on ordinary differential equations and relies on previously introduced models on the bovine estrous cycle and the glucose-insulin dynamics. Necessary modifications and coupling mechanisms are thoroughly discussed. Depending on the composition and the amount of feed, in particular the glucose content in the dry matter, the model quantifies reproductive hormones and follicular development over time. Simulation results for different nutritional regimes in lactating and non-lactating dairy cows are examined and compared with experimental studies. The simulations describe realistically the effects of nutritional glucose supply on the ovulatory cycle of dairy cattle. Conclusions The mathematical model enables the user to explore the relationship between nutrition and reproduction by running simulations and performing parameter studies. Regarding its applicability, this work is an early attempt towards developing in silico feeding strategies and may eventually help to refine and reduce animal experiments. Reviewers This article was reviewed by John McNamara and Tin Pang (nominated by Martin Lercher).

In this paper, we develop a mathematical model for the spread and control of the coronavirus disease. An outbreak of COVID-19 has led to more than one million confirmed cases as of April 3rd, 2020. Understanding the early spread dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe COVID-19 spread with four datasets from within and outside of Wuhan, China; it is estimated how spread in Wuhan varied between January and February 2020. It is used these estimates to assess the potential for sustained human-to-human spread to occur in locations outside Wuhan if disease holders were introduced. It is combined SEIR framework model with data on cases of COVID-19 in China and International cases that originated in Wuhan to estimate how spread had varied over time during January and February 2020. Based on these estimates, it is calculated the probability that freshly introduced cases might produce outbreaks in other regions. Also, it is calculated approximately the median day by day basic reproduction number in Wuhan, refused from 2·45 (95% CI: 1·16–4·87) one week before travel restrictions were introduced on Jan 23rd, 2020, to 1.05 (0·42–2·40) one week after. Based on our estimates of, presumptuous SARS approximating disparity, it is computed that in locations with a similar spread potential to Wuhan in near the beginning of January, some time ago there are at least four independently set up cases, there is a more than fifty percent chance the infection will found within those inhabitants. COVID-19 spreading probably refused in Wuhan during delayed January 2020, corresponding with the prologue of voyage control channels. As more cases arrive in international locations with similar spread potential to Wuhan, before these organize measures, it is likely many chains of spread will fail to create initially but might lead to innovative outbreaks ultimately.

Highlights • Models capturing key malaria life-cycle stages can help us evaluate vaccine candidates. • Model fitting revealed antibody avidity to be an important determinant of RTS,S vaccine efficacy. • High avidity and titre were associated with increased levels of vaccine efficacy. • Did not identify any thresholds of protection for either immune marker., Anti-circumsporozoite antibody titres have been established as an essential indicator for evaluating the immunogenicity and protective capacity of the RTS,S/AS01 malaria vaccine. However, a new delayed-fractional dose regime of the vaccine was recently shown to increase vaccine efficacy, from 62.5% (95% CI 29.4–80.1%) under the original dosing schedule to 86.7% (95% CI, 66.8–94.6%) without a corresponding increase in antibody titres. Here we reanalyse the antibody data from this challenge trial to determine whether IgG avidity may help to explain efficacy better than IgG titre alone by adapting a within-host mathematical model of sporozoite inoculation. We demonstrate that a model incorporating titre and avidity provides a substantially better fit to the data than titre alone. These results also suggest that in individuals with a high antibody titre response that also show high avidity (both metrics in the top tercile of observed values) delayed-fractional vaccination provided near perfect protection upon first challenge (98.2% [95% Credible Interval 91.6–99.7%]). This finding suggests that the quality of the vaccine induced antibody response is likely to be an important determinant in the development of highly efficacious pre-erythrocytic vaccines against malaria.

Individual cells exhibit specific proliferative responses to changes in microenvironmental conditions. Whether such potential is constrained by the cell density throughout the growth process is however unclear. Here, we identify a theoretical framework that captures how the information encoded in the initial density of cancer cell populations impacts their growth profile. By following the growth of hundreds of populations of cancer cells, we found that the time they need to adapt to the environment decreases as the initial cell density increases. Moreover, the population growth rate shows a maximum at intermediate initial densities. With the support of a mathematical model, we show that the observed interdependence of adaptation time and growth rate is significantly at odds both with standard logistic growth models and with the Monod-like function that governs the dependence of the growth rate on nutrient levels. Our results (i) uncover and quantify a previously unnoticed heterogeneity in the growth dynamics of cancer cell populations; (ii) unveil how population growth may be affected by single-cell adaptation times; (iii) contribute to our understanding of the clinically-observed dependence of the primary and metastatic tumor take rates on the initial density of implanted cancer cells., 25 pages (13 main text + 12 supplementary information), 16 figures (5 main text + 11 supplementary information). Comments welcome

A global response to the rapid spread of the 2019 novel coronavirus disease (COVID-19) is imperative in order to reduce mortality and morbidity as well as preventing a country’s health system from collapse. Singapore showed exceptional leadership in the containment of the spread of the virus, however through April 2020 the country experienced exponential growth in the number of infections, particularly migrant workers living in dormitories. The following historical case study provides an overview of Singapore’s country profile, their healthcare system and the country’s non pharmaceutical measures taken to mitigate and contain the spread of COVID-19 in the first few months of the pandemic. We explore the impact COVID-19 had on Singapore’s economy at that time and the implications of the resultant social and political disruptions. We conclude our study by using mathematical modelling to explore confirmed COVID-19 cases in Singapore’s local community and those living in dormitories and use this data to forecast the progression of the epidemic in Singapore given the non-pharmaceutical interventions in place at that time. Our results indicate the COVID-19 outbreak in Singapore increased 3-fold the initial doubling rate of 22.5 days in the first 2 months of the outbreak to 6.7 days in the 5th month; We note a faster doubling rate of 4.9 days for those living in dormitories compared to a doubling rate of 13.5 days for the rest of the community.

Circadian rhythms in mammals are orchestrated by a central clock within the suprachiasmatic nuclei (SCN). Our understanding of the electrophysiological basis of SCN activity comes overwhelmingly from a small number of nocturnal rodent species, and the extent to which these are retained in day-active animals remains unclear. Here, we recorded the spontaneous and evoked electrical activity of single SCN neurons in the diurnal rodent Rhabdomys pumilio, and developed cutting-edge data assimilation and mathematical modeling approaches to uncover the underlying ionic mechanisms. As in nocturnal rodents, R. pumilio SCN neurons were more excited during daytime hours. By contrast, the evoked activity of R. pumilio neurons included a prominent suppressive response that is not present in the SCN of nocturnal rodents. Our modeling revealed and subsequent experiments confirmed transient subthreshold A-type potassium channels as the primary determinant of this response, and suggest a key role for this ionic mechanism in optimizing SCN function to accommodate R. pumilio’s diurnal niche.

Synaptic connections in many brain circuits fluctuate, exhibiting substantial turnover and remodelling over hours to days. Surprisingly, experiments show that most of this flux in connectivity persists in the absence of learning or known plasticity signals. How can neural circuits retain learned information despite a large proportion of ongoing and potentially disruptive synaptic changes? We address this question from first principles by analysing how much compensatory plasticity would be required to optimally counteract ongoing fluctuations, regardless of whether fluctuations are random or systematic. Remarkably, we find that the answer is largely independent of plasticity mechanisms and circuit architectures: compensatory plasticity should be at most equal in magnitude to fluctuations, and often less, in direct agreement with previously unexplained experimental observations. Moreover, our analysis shows that a high proportion of learning-independent synaptic change is consistent with plasticity mechanisms that accurately compute error gradients.

BackgroundElevated rates of tuberculosis in healthcare workers demonstrate the high rate of Mycobacterium tuberculosis (Mtb) transmission in health facilities in high-burden settings. In the context of a project taking a whole systems approach to tuberculosis infection prevention and control (IPC), we aimed to evaluate the potential impact of conventional and novel IPC measures on Mtb transmission to patients and other clinic attendees.MethodsAn individual-based model of patient movements through clinics, ventilation in waiting areas, and Mtb transmission was developed, and parameterised using empirical data from eight clinics in two provinces in South Africa. Seven interventions—codeveloped with health professionals and policy-makers—were simulated: (1) queue management systems with outdoor waiting areas, (2) ultraviolet germicidal irradiation (UVGI) systems, (3) appointment systems, (4) opening windows and doors, (5) surgical mask wearing by clinic attendees, (6) simple clinic retrofits and (7) increased coverage of long antiretroviral therapy prescriptions and community medicine collection points through the Central Chronic Medicine Dispensing and Distribution (CCMDD) service.ResultsIn the model, (1) outdoor waiting areas reduced the transmission to clinic attendees by 83% (IQR 76%–88%), (2) UVGI by 77% (IQR 64%–85%), (3) appointment systems by 62% (IQR 45%–75%), (4) opening windows and doors by 55% (IQR 25%–72%), (5) masks by 47% (IQR 42%–50%), (6) clinic retrofits by 45% (IQR 16%–64%) and (7) increasing the coverage of CCMDD by 22% (IQR 12%–32%).ConclusionsThe majority of the interventions achieved median reductions in the rate of transmission to clinic attendees of at least 45%, meaning that a range of highly effective intervention options are available, that can be tailored to the local context. Measures that are not traditionally considered to be IPC interventions, such as appointment systems, may be as effective as more traditional IPC measures, such as mask wearing.

International audience; Kisspeptin (Kp), a family of peptides comprising products of the Kiss1 gene, was discovered 20 years ago; it is recognised as the major factor controlling the activity of the gonadotrophin-releasing hormone (GnRH) neurones and thus the activation of the reproductive axis in mammals. It has been widely documented that the effects of Kp on reproduction through its action on GnRH neurones are mediated by the GPR54 receptor. Kp controls the activation of the reproductive axis at puberty, maintains reproductive axis activity in adults and is involved in triggering ovulation in some species. Although there is ample evidence coming from both conditional knockout models and conditional-induced Kp neurone death implicating the Kp/GPR54 pathway in the control of reproduction, the mechanism(s) underlying this process may be more complex than a sole direct control of GnRH neuronal activity by Kp. In this review, we provide an overview of the recent advances made in elucidating the interplay between Kp- and GnRH- neuronal networks with respect to regulating the reproductive axis. We highlight the existence of a possible mutual regulation between GnRH and Kp neurones, as well as the implication of Kp-dependent volume transmission in this process. We also discuss the capacity of heterodimerisation between GPR54 and GnRH receptor (GnRH-R) and its consequences on signalling. Finally, we illustrate the role of mathematical modelling that accounts for the synergy between GnRH-R and GPR54 in explaining the role of these two receptors when defining GnRH neuronal activity and GnRH pulsatile release.

The management of infectious diseases in wildlife reservoirs is challenging and faces several limitations. However, detailed knowledge of host–pathogen systems often reveal heterogeneity among the hosts’ contribution to transmission. Management strategies targeting specific classes of individuals and/or areas, having a particular role in transmission, could be more effective and more acceptable than population-wide interventions. In the wild population of Alpine ibex (Capra ibex—a protected species) of the Bargy massif (French Alps), females transmit brucellosis (Brucella melitensis) infection in ~90% of cases, and most transmissions occur in the central spatial units (“core area”). Therefore, we expanded an individual-based model, developed in a previous study, to test whether strategies targeting females or the core area, or both, would be more effective. We simulated the relative efficacy of realistic strategies for the studied population, combining test-and-remove (euthanasia of captured animals with seropositive test results) and partial culling of unmarked animals. Targeting females or the core area was more effective than untargeted management options, and strategies targeting both were even more effective. Interestingly, the number of ibex euthanized and culled in targeted strategies were lower than in untargeted ones, thus decreasing the conservation costs while increasing the sanitary benefits. Although there was no silver bullet for the management of brucellosis in the studied population, targeted strategies offered a wide range of promising refinements to classical sanitary measures. We therefore encourage to look for heterogeneity in other wildlife diseases and to evaluate potential strategies for improving management in terms of efficacy but also acceptability. Supplementary Information The online version contains supplementary material available at 10.1186/s13567-021-00984-0.

To effectively navigate complex tissue microenvironments, immune cells sense molecular concentration gradients using G-protein coupled receptors. However, due to the complexity of receptor activity, and the multimodal nature of chemokine gradients in vivo, chemokine receptor activity in situ is poorly understood. To address this issue, we apply a modelling and simulation approach that permits analysis of the spatiotemporal dynamics of CXCR5 expression within an in silico B-follicle with single-cell resolution. Using this approach, we show that that in silico B-cell scanning is robust to changes in receptor numbers and changes in individual kinetic rates of receptor activity, but sensitive to global perturbations where multiple parameters are altered simultaneously. Through multi-objective optimization analysis we find that the rapid modulation of CXCR5 activity through receptor binding, desensitization and recycling is required for optimal antigen scanning rates. From these analyses we predict that chemokine receptor signaling dynamics regulate migration in complex tissue microenvironments to a greater extent than the total numbers of receptors on the cell surface.

Background:Vaccination is one of the most effective public health interventions. We investigate the impact of vaccination activities for Haemophilus influenzae type b, hepatitis B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, rotavirus, rubella, Streptococcus pneumoniae, and yellow fever over the years 2000–2030 across 112 countries.Methods:Twenty-one mathematical models estimated disease burden using standardised demographic and immunisation data. Impact was attributed to the year of vaccination through vaccine-activity-stratified impact ratios.Results:We estimate 97 (95%CrI[80, 120]) million deaths would be averted due to vaccination activities over 2000–2030, with 50 (95%CrI[41, 62]) million deaths averted by activities between 2000 and 2019. For children under-5 born between 2000 and 2030, we estimate 52 (95%CrI[41, 69]) million more deaths would occur over their lifetimes without vaccination against these diseases.Conclusions:This study represents the largest assessment of vaccine impact before COVID-19-related disruptions and provides motivation for sustaining and improving global vaccination coverage in the future.Funding:VIMC is jointly funded by Gavi, the Vaccine Alliance, and the Bill and Melinda Gates Foundation (BMGF) (BMGF grant number: OPP1157270 / INV-009125). Funding from Gavi is channelled via VIMC to the Consortium’s modelling groups (VIMC-funded institutions represented in this paper: Imperial College London, London School of Hygiene and Tropical Medicine, Oxford University Clinical Research Unit, Public Health England, Johns Hopkins University, The Pennsylvania State University, Center for Disease Analysis Foundation, Kaiser Permanente Washington, University of Cambridge, University of Notre Dame, Harvard University, Conservatoire National des Arts et Métiers, Emory University, National University of Singapore). Funding from BMGF was used for salaries of the Consortium secretariat (authors represented here: TBH, MJ, XL, SE-L, JT, KW, NMF, KAMG); and channelled via VIMC for travel and subsistence costs of all Consortium members (all authors). We also acknowledge funding from the UK Medical Research Council and Department for International Development, which supported aspects of VIMC's work (MRC grant number: MR/R015600/1).JHH acknowledges funding from National Science Foundation Graduate Research Fellowship; Richard and Peggy Notebaert Premier Fellowship from the University of Notre Dame. BAL acknowledges funding from NIH/NIGMS (grant number R01 GM124280) and NIH/NIAID (grant number R01 AI112970). The Lives Saved Tool (LiST) receives funding support from the Bill and Melinda Gates Foundation.This paper was compiled by all coauthors, including two coauthors from Gavi. Other funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.

IntroductionSkewed levels of the sex ratio at birth (SRB) due to sex-selective abortions have been observed in several countries since the 1970s. They will lead to long-term sex imbalances in more than one-third of the world’s population with yet unknown social and economic impacts on affected countries. Understanding the potential evolution of sex imbalances at birth is therefore essential for anticipating and planning for changing sex structures across the world.MethodsWe produced probabilistic SRB projections from 2021 to 2100 based on different scenarios of sex ratio transition and assessed their implications in terms of missing female births at global, regional and national levels. Based on a comprehensive SRB database with 3.26 billion birth records, we project the skewed SRB and missing female births with a Bayesian hierarchical time series mixture model. The SRB projections under reference scenario S1 assumed SRB transitions only for countries with strong statistical evidence of SRB inflation, and the more extreme scenario S2 assumed a sex ratio transition for countries at risk of SRB inflation but with no or limited evidence of ongoing inflation.ResultsUnder scenario S1, we projected 5.7 (95% uncertainty interval (1.2; 15.3)) million additional missing female births to occur by 2100. Countries affected will be those already affected in the past by imbalanced SRB, such as China and India. If all countries at risk of SRB inflation experience a sex ratio transition as in scenario S2, the projected missing female births increase to 22.1 (12.2; 39.8) million with a sizeable contribution of sub-Saharan Africa.ConclusionThe scenario-based projections provide important illustrations of the potential burden of future prenatal sex discrimination and the need to monitor SRBs in countries with son preference. Policy planning will be needed in the years to come to minimise future prenatal sex discrimination and its impact on social structures.

Summary Background Childhood immunisation services have been disrupted by the COVID-19 pandemic. WHO recommends considering outbreak risk using epidemiological criteria when deciding whether to conduct preventive vaccination campaigns during the pandemic. Methods We used 2-3 models per infection to estimate the health impact of 50% reduced routine vaccination coverage in 2020 and delay of campaign vaccination from 2020 to 2021 for measles vaccination in Bangladesh, Chad, Ethiopia, Kenya, Nigeria, and South Sudan, for meningococcal A vaccination in Burkina Faso, Chad, Niger, and Nigeria, and for yellow fever vaccination in the Democratic Republic of Congo, Ghana, and Nigeria. Our counterfactual comparative scenario was sustaining immunisation services at coverage projections made prior to COVID-19 (i.e. without any disruption). Findings Reduced routine vaccination coverage in 2020 without catch-up vaccination may lead to an increase in measles and yellow fever disease burden in the modelled countries. Delaying planned campaigns in Ethiopia and Nigeria by a year may significantly increased the risk of measles outbreaks (both countries did complete their SIAS planned for 2020). For yellow fever vaccination, delay in campaigns leads to a potential disease burden rise of >1 death per 100,000 people until the campaigns are implemented. For meningococcal A vaccination, short term disruptions in 2020 are unlikely to have a significant impact due to the persistence of direct and indirect benefits from past introductory campaigns of the 1 to 29-year-old population, bolstered by inclusion of the vaccine into the routine immunisation schedule accompanied by further catch-up campaigns. Interpretation The impact of COVID-19-related disruption to vaccination programs varies between infections and countries. Planning and implementation of campaigns should consider country and infection-specific epidemiological factors and local immunity gaps worsened by the COVID-19 pandemic when prioritising vaccines and strategies for catch-up vaccination. Funding Bill & Melinda Gates Foundation and Gavi, the Vaccine Alliance Research in context Evidence before the study We searched PubMed for (COVID-19 OR coronavirus OR SARS-CoV-2) AND (child health intervention OR vaccin* or immuni*) AND (disruption OR suspension OR reduction) AND (indirect effect OR health impact) on January 14, 2021, with no language restrictions. We found 178 articles of which 13 articles were relevant. Six articles reported some empirical data on immunization disruption in Bangladesh, Japan, Kenya, Nigeria, Pakistan, Saudi Arabia, South Africa, Spain and Italy, and a survey study focussed on immunization disruption in low and middle-income countries. One article proposed using the WHO health systems framework to assess the effects of COVID-19 on immunisation programmes in South Africa, another study on leveraging systems thinking and implementation science to improve immunization system performance in Africa, and two studies were review articles. One modelling study focused on the indirect effects, including reduction in routine immunisation services, of the COVID-19 pandemic on maternal and child mortality in low-income and middle-income countries. Another modelling study focused on a benefit-risk analysis of routine childhood immunisation during the COVID-19 pandemic in Africa. We also found one other study in the grey literature that analysed the impact on SARS-CoV-2 infections as a result of fixed-post and door-to-door vaccination campaigns targeted at children under five years of age in an Ethiopia-like setting. Added value of this study We estimated the increase in cases and deaths caused by the disruption to immunisation services leading to outbreaks for measles, meningococcal A and yellow fever in 10 countries. The reduction in routine immunisation coverage among under-immunised cohorts of children has enhanced the risk of outbreaks which cannot be averted without catch-up vaccination. This can lead to an increase of 9.89% (0.91 additional deaths per 100,000 individuals, or 48,000 in total) across the three diseases from 2020 to 2030 in the countries considered. Results vary by infection and country, but generally most excess deaths are due to measles. Postponing campaign immunisation may not have a detrimental short-term health impact if campaign immunisation is implemented ahead of future outbreaks caused by these immunity gaps. Implications of the available evidence The COVID-19 pandemic has caused significant disruptions to routine services and vaccination campaigns and resulted in immunity gaps with potential to cause outbreaks in the affected populations. The short-term and long-term health impact differ between measles, meningococcal A and yellow fever vaccination and by countries based on the local epidemiological situation. Thereby, catch-up vaccination should be planned by considering the heterogeneity in population susceptibility across different countries to measles, meningococcal A and yellow fever outbreaks and implemented in time to prevent these outbreaks. The study findings can inform risk-benefit trade-off discussions around the timing of campaigns.

The aim of this study was to further evaluate and optimize the Transwell® system for assessing the dissolution behavior of orally inhaled drug products (OIDPs), using fluticasone propionate as a model drug. Sample preparation involved the collection of a relevant inhalable dose fraction through an anatomical mouth/throat model, resulting in a more uniform presentation of drug particles during the subsequent dissolution test. The method differed from previously published procedures by (1) using a 0.4 µm polycarbonate (PC) membrane, (2) stirring the receptor compartment, and (3) placing the drug-containing side of the filter paper face downwards, towards the PC membrane. A model developed in silico, paired with the results of in vitro studies, suggested that a dissolution medium providing a solubility of about 5 µg/mL would be a good starting point for the method’s development, resulting in mean transfer times that were about 10 times longer than those of a solution. Furthermore, the model suggested that larger donor/receptor and sampling volumes (3, 3.3 and 2 mL, respectively) will significantly reduce the so-called “mass effect”. The outcomes of this study shed further light on the impact of experimental conditions on the complex interplay of dissolution and diffusion within a volume-limited system, under non-sink conditions.

By June 2021, a new contagious disease, the Coronavirus disease 2019 (COVID-19), has infected more than 172 million people worldwide, causing more than 3.7 million deaths. Many aspects related to the interactions of the disease’s causative agent, SAR2-CoV-2, and the immune response are not well understood: the multiscale interactions among the various components of the human immune system and the pathogen are very complex. Mathematical and computational tools can help researchers to answer these open questions about the disease. In this work, we present a system of fifteen ordinary differential equations that models the immune response to SARS-CoV-2. The model is used to investigate the hypothesis that the SARS-CoV-2 infects immune cells and, for this reason, induces high-level productions of inflammatory cytokines. Simulation results support this hypothesis and further explain why survivors have lower levels of cytokines levels than non-survivors.

The number of COVID-19 outbreaks reported in UK care homes rose rapidly in early March of 2020. Owing to the increased co-morbidities and therefore worse COVID-19 outcomes for care home residents, it is important that we understand this increase and its future implications. We demonstrate the use of an SIS model where each nursing home is an infective unit capable of either being susceptible to an outbreak (S) or in an active outbreak (I). We use a generalized additive model to approximate the trend in growth rate of outbreaks in care homes and find the fit to be improved in a model where the growth rate is proportional to the number of current care home outbreaks compared with a model with a constant growth rate. Using parameters found from the outbreak-dependent growth rate, we predict a 73% prevalence of outbreaks in UK care homes without intervention as a reasonable worst-case planning assumption. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

In this paper, we explore the application of Chimeric Antigen Receptor (CAR) T cell therapy for the treatment of Acute Lymphocytic Leukaemia (ALL) by means of in silico experimentation, mathematical modelling through first-order Ordinary Differential Equations and nonlinear systems theory. By combining the latter with systems biology on cancer evolution we were able to establish a sufficient condition on the therapy dose to ensure complete response. The latter is illustrated across multiple numerical simulations when comparing three mathematically formulated administration protocols with one of a phase 1 dose-escalation trial on CAR-T cells for the treatment of ALL on children and young adults. Therefore, both our analytical and in silico results are consistent with real-life scenarios. Finally, our research indicates that tumour cells growth rate and the killing efficacy of the therapy are key factors in the designing of personalised strategies for cancer treatment.

Background:Sanofi-Pasteur’s CYD-TDV is the only licensed dengue vaccine. Two phase three trials showed higher efficacy in seropositive than seronegative recipients. Hospital follow-up revealed increased hospitalisation in 2–5- year-old vaccinees, where serostatus and age effects were unresolved.Methods:We fit a survival model to individual-level data from both trials, including year 1 of hospital follow-up. We determine efficacy by age, serostatus, serotype and severity, and examine efficacy duration and vaccine action mechanism.Results:Our modelling indicates that vaccine-induced immunity is long-lived in seropositive recipients, and therefore that vaccinating seropositives gives higher protection than two natural infections. Long-term increased hospitalisation risk outweighs short-lived immunity in seronegatives. Independently of serostatus, transient immunity increases with age, and is highest against serotype 4. Benefit is higher in seropositives, and risk enhancement is greater in seronegatives, against hospitalised disease than against febrile disease.Conclusions:Our results support vaccinating seropositives only. Rapid diagnostic tests would enable viable ‘screen-then-vaccinate’ programs. Since CYD-TDV acts as a silent infection, long-term safety of other vaccine candidates must be closely monitored.Funding:Bill & Melinda Gates Foundation, National Institute for Health Research, UK Medical Research Council, Wellcome Trust, Royal Society.Clinical trial number:NCT01373281 and NCT01374516.

Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide. This study aimed to assess and predict the incidence of COVID-19 in Thailand, including the preparation and evaluation of intervention strategies. An SEIR (susceptible, exposed, infected, recovered) model was implemented with model parameters estimated using the Bayesian approach. The model&rsquo, s projections showed that the highest daily reported incidence of COVID-19 would be approximately 140 cases (95% credible interval, CrI: 83&ndash, 170 cases) by the end of March 2020. After Thailand declared an emergency decree, the numbers of new cases and case fatalities decreased, with no new imported cases. According to the model&rsquo, s predictions, the incidence would be zero at the end of June if non-pharmaceutical interventions (NPIs) were strictly and widely implemented. These stringent NPIs reduced the effective reproductive number (Rt) to 0.73 per day (95% CrI: 0.53&ndash, 0.93) during April and May. Sensitivity analysis showed that contact rate, hand washing, and face mask wearing effectiveness were the parameters that most influenced the number of reported daily new cases. Our evaluation shows that Thailand&rsquo, s intervention strategies have been highly effective in mitigating disease propagation. Continuing with these strict disease prevention behaviors could minimize the risk of a new COVID-19 outbreak in Thailand.

Epidemic data show the existence of a region of quasi-linear growth (strolling period) of infected cases extending in between waves. We demonstrate that this constitutes evidence for the existence of near time-scale invariance that is neatly encoded via complex fixed points in the epidemic Renormalisation Group approach. As a result we achieve a deeper understanding of multiple wave dynamics and its inter-wave strolling regime. Our results are tested and calibrated against the COVID-19 pandemic data. Because of the simplicity of our approach that is organised around symmetry principles our discovery amounts to a paradigm shift in the way epidemiological data are mathematically modelled., Comment: 9 pages, 4 figures