Multiple pandemic waves vs multi-period/multi-phasic epidemics: Global shape of the COVID-19 pandemic.

The overall course of the COVID-19 pandemic in Western countries has been characterized by complex sequences of phases. In the period before the arrival of vaccines, these phases were mainly due to the alternation between the strengthening/lifting of social distancing measures, with the aim to balance the protection of health and that of the society as a whole. After the arrival of vaccines, this multi-phasic character was further emphasized by the complicated deployment of vaccination campaigns and the onset of virus' variants. To cope with this multi-phasic character, we propose a theoretical approach to the modeling of overall pandemic courses, that we term multi-period/multi-phasic, based on a specific definition of phase. This allows a unified and parsimonious representation of complex epidemic courses even when vaccination and virus' variants are considered, through sequences of weak ergodic renewal equations that become fully ergodic when appropriate conditions are met. Specific hypotheses on epidemiological and intervention parameters allow reduction to simple models. The framework suggest a simple, theory driven, approach to data explanation that allows an accurate reproduction of the overall course of the COVID-19 epidemic in Italy since its beginning (February 2020) up to omicron onset, confirming the validity of the concept.

A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing.

Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these states within an evolutionary context on genetic clones. Here we present CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles onto the latent space of copy number clones. CONGAS+ clusters cells into tumour subclones with similar ploidy, rendering straightforward to compare their expression and chromatin profiles. The framework, implemented on GPU and tested on real and simulated data, scales to analyse seamlessly thousands of cells, demonstrating better performance than single-molecule models, and supporting new multi-omics assays. In prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability.

Multiple epidemic waves as the outcome of stochastic SIR epidemics with behavioral responses: a hybrid modeling approach.

In the behavioral epidemiology (BE) of infectious diseases, little theoretical effort seems to have been devoted to understand the possible effects of individuals' behavioral responses during an epidemic outbreak in small populations. To fill this gap, here we first build general, behavior implicit, SIR epidemic models including behavioral responses and set them within the framework of nonlinear feedback control theory. Second, we provide a thorough investigation of the effects of different types of agents' behavioral responses for the dynamics of hybrid stochastic SIR outbreak models. In the proposed model, the stochastic discrete dynamics of infection spread is combined with a continuous model describing the agents' delayed behavioral response. The delay reflects the memory mechanisms with which individuals enact protective behavior based on past data on the epidemic course. This results in a stochastic hybrid system with time-varying transition probabilities. To simulate such system, we extend Gillespie's classic stochastic simulation algorithm by developing analytical formulas valid for our classes of models. The algorithm is used to simulate a number of stochastic behavioral models and to classify the effects of different types of agents' behavioral responses. In particular this work focuses on the effects of the structure of the response function and of the form of the temporal distribution of such response. Among the various results, we stress the appearance of multiple, stochastic epidemic waves triggered by the delayed behavioral response of individuals.

J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments.

BACKGROUND: The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods. RESULT: We introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats. CONCLUSION: J-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data. J-SPACE is freely available at: https://github.com/BIMIB-DISCo/J-Space.jl .

A behavioural modelling approach to assess the impact of COVID-19 vaccine hesitancy.

We introduce a compartmental epidemic model to describe the spread of COVID-19 within a population, assuming that a vaccine is available, but vaccination is not mandatory. The model takes into account vaccine hesitancy and the refusal of vaccination by individuals, which take their decision on vaccination based on both the present and past information about the spread of the disease. Theoretical analysis and simulations show that voluntary vaccination can certainly reduce the impact of the disease but is unable to eliminate it. We also demonstrate how the information-related parameters affect the dynamics of the disease. In particular, vaccine hesitancy and refusal are better contained in case of widespread information coverage and short-term memory. Finally, the possible impact of seasonality on the spread of the disease is investigated.

Optimal time-profiles of public health intervention to shape voluntary vaccination for childhood diseases.

In order to seek the optimal time-profiles of public health systems (PHS) Intervention to favor vaccine propensity, we apply optimal control (OC) to a SIR model with voluntary vaccination and PHS intervention. We focus on short-term horizons, and on both continuous control strategies resulting from the forward-backward sweep deterministic algorithm, and piecewise-constant strategies (which are closer to the PHS way of working) investigated by the simulated annealing (SA) stochastic algorithm. For childhood diseases, where disease costs are much larger than vaccination costs, the OC solution sets at its maximum for most of the policy horizon, meaning that the PHS cannot further improve perceptions about the net benefit of immunization. Thus, the subsequent dynamics of vaccine uptake stems entirely from the declining perceived risk of infection (due to declining prevalence) which is communicated by direct contacts among parents, and unavoidably yields a future decline in vaccine uptake. We find that for relatively low communication costs, the piecewise control is close to the continuous control. For large communication costs the SA algorithm converges towards a non-monotone OC that can have oscillations.

Endogenous and exogenous testosterone and the risk of prostate cancer and increased prostate-specific antigen (PSA) level: a meta-analysis.

OBJECTIVE: To review and quantify the association between endogenous and exogenous testosterone and prostate-specific antigen (PSA) and prostate cancer. METHODS: Literature searches were performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Prospective cohort studies that reported data on the associations between endogenous testosterone and prostate cancer, and placebo-controlled randomized trials of testosterone replacement therapy (TRT) that reported data on PSA and/or prostate cancer cases were retained. Meta-analyses were performed using random-effects models, with tests for publication bias and heterogeneity. RESULTS: Twenty estimates were included in a meta-analysis, which produced a summary relative risk (SRR) of prostate cancer for an increase of 5 nmol/L of testosterone of 0.99 (95% confidence interval [CI] 0.96, 1.02) without heterogeneity (I² = 0%). Based on 26 trials, the overall difference in PSA levels after onset of use of TRT was 0.10 ng/mL (-0.28, 0.48). Results were similar when conducting heterogeneity analyses by mode of administration, region, age at baseline, baseline testosterone, trial duration, type of patients and type of TRT. The SRR of prostate cancer as an adverse effect from 11 TRT trials was 0.87 (95% CI 0.30; 2.50). Results were consistent across studies. CONCLUSIONS: Prostate cancer appears to be unrelated to endogenous testosterone levels. TRT for symptomatic hypogonadism does not appear to increase PSA levels nor the risk of prostate cancer development. The current data are reassuring, although some caution is essential until multiple studies with longer follow-up are available.

The role of stochastic gene switching in determining the pharmacodynamics of certain drugs: basic mechanisms.

In this paper we analyze the impact of the stochastic fluctuation of genes between their ON and OFF states on the pharmacodynamics of a potentially large class of drugs. We focus on basic mechanisms underlying the onset of in vitro experimental dose-response curves, by investigating two elementary molecular circuits. Both circuits consist in the transcription of a gene and in the successive translation into the corresponding protein. Whereas in the first the activation/deactivation rates of the single gene copy are constant, in the second the protein, now a transcription factor, amplifies the deactivation rate, so introducing a negative feedback. The drug is assumed to enhance the elimination of the protein, and in both cases the success of therapy is assured by keeping the level of the given protein under a threshold for a fixed time. Our numerical simulations suggests that the gene switching plays a primary role in determining the sigmoidal shape of dose-response curves. Moreover, the simulations show interesting phenomena related to the magnitude of the average gene switching time and to the drug concentration. In particular, for slow gene switching a significant fraction of cells can respond also in the absence of drug or with drug concentrations insufficient for the response in a deterministic setting. For higher drug concentrations, the non-responding fraction exhibits a maximum at intermediate values of the gene switching rates. For fast gene switching, instead, the stochastic prediction follows the prediction of the deterministic approximation, with all the cells responding or non-responding according to the drug dose.

The pharmacodynamics of the p53-Mdm2 targeting drug Nutlin: the role of gene-switching noise.

In this work we investigate, by means of a computational stochastic model, how tumor cells with wild-type p53 gene respond to the drug Nutlin, an agent that interferes with the Mdm2-mediated p53 regulation. In particular, we show how the stochastic gene-switching controlled by p53 can explain experimental dose-response curves, i.e., the observed inter-cell variability of the cell viability under Nutlin action. The proposed model describes in some detail the regulation network of p53, including the negative feedback loop mediated by Mdm2 and the positive loop mediated by PTEN, as well as the reversible inhibition of Mdm2 caused by Nutlin binding. The fate of the individual cell is assumed to be decided by the rising of nuclear-phosphorylated p53 over a certain threshold. We also performed in silico experiments to evaluate the dose-response curve after a single drug dose delivered in mice, or after its fractionated administration. Our results suggest that dose-splitting may be ineffective at low doses and effective at high doses. This complex behavior can be due to the interplay among the existence of a threshold on the p53 level for its cell activity, the nonlinearity of the relationship between the bolus dose and the peak of active p53, and the relatively fast elimination of the drug.

Epidemic control by social distancing and vaccination: Optimal strategies and remarks on the COVID-19 Italian response policy.

After the many failures in the control of the COVID-19 pandemic, identifying robust principles of epidemic control will be key in future preparedness. In this work, we propose an optimal control model of an age-of-infection transmission model under a two-phase control regime where social distancing is the only available control tool in the first phase, while the second phase also benefits from the arrival of vaccines. We analyzed the problem by an ad-hoc numerical algorithm under a strong hypothesis implying a high degree of prioritization to the protection of health from the epidemic attack, which we termed the "low attack rate" hypothesis. The outputs of the model were also compared with the data from the Italian COVID-19 experience to provide a crude assessment of the goodness of the enacted interventions prior to the onset of the Omicron variant.

Computational validation of clonal and subclonal copy number alterations from bulk tumor sequencing using CNAqc.

Copy number alterations (CNAs) are among the most important genetic events in cancer, but their detection from sequencing data is challenging because of unknown sample purity, tumor ploidy, and general intra-tumor heterogeneity. Here, we present CNAqc, an evolution-inspired method to perform the computational validation of clonal and subclonal CNAs detected from bulk DNA sequencing. CNAqc is validated using single-cell data and simulations, is applied to over 4000 TCGA and PCAWG samples, and is incorporated into the validation process for the clinically accredited bioinformatics pipeline at Genomics England. CNAqc is designed to support automated quality control procedures for tumor somatic data validation.

Multifaceted kinetics of immuno-evasion from tumor dormancy.

Tumor progression is subject to modulation by the immune system. The immune system can eliminate tumors or keep them at a dormant equilibrium size, while some tumors escape immunomodulation and advance to malignancy. Herein, we discuss some aspects of immune evasion of dormant tumors from a theoretical biophysics point of view that can be modeled mathematically. We go on to analyze the mathematical system on multiple timescales. First, we consider a long timescale where tumor evasion is likely due to adaptive (and somewhat deterministic) immuno-editing. Then, we consider the temporal mesoscale and hypothesize that extrinsic noise could be a major factor in induction of immuno-evasion. Implications of immuno-evasive mechanisms for the outcome of immunotherapies are also discussed. In addition, we discuss the ideas that population level tumor dormancy may not be a quiescence phenomenon and that dormant tumors can, at least if modulated by the immune system, live a very active and noisy life!

Testing a simple and frugal model of health protective behaviour in epidemic times.

The COVID-19 epidemic highlighted the necessity to integrate dynamic human behaviour change into infectious disease transmission models. The adoption of health protective behaviour, such as handwashing or staying at home, depends on both epidemiological and personal variables. However, only a few models have been proposed in the recent literature to account for behavioural change in response to the health threat over time. This study aims to estimate the relevance of TELL ME, a simple and frugal agent-based model developed following the 2009 H1N1 outbreak to explain individual engagement in health protective behaviours in epidemic times and how communication can influence this. Basically, TELL ME includes a behavioural rule to simulate individual decisions to adopt health protective behaviours. To test this rule, we used behavioural data from a series of 12 cross-sectional surveys in France over a 6-month period (May to November 2020). Samples were representative of the French population (N = 24,003). We found the TELL ME behavioural rule to be associated with a moderate to high error rate in representing the adoption of behaviours, indicating that parameter values are not constant over time and that other key variables influence individual decisions. These results highlight the crucial need for longitudinal behavioural data to better calibrate epidemiological models accounting for public responses to infectious disease threats.

Fine-tuning anti-tumor immunotherapies via stochastic simulations.

BACKGROUND: Anti-tumor therapies aim at reducing to zero the number of tumor cells in a host within their end or, at least, aim at leaving the patient with a sufficiently small number of tumor cells so that the residual tumor can be eradicated by the immune system. Besides severe side-effects, a key problem of such therapies is finding a suitable scheduling of their administration to the patients. In this paper we study the effect of varying therapy-related parameters on the final outcome of the interplay between a tumor and the immune system. RESULTS: This work generalizes our previous study on hybrid models of such an interplay where interleukins are modeled as a continuous variable, and the tumor and the immune system as a discrete-state continuous-time stochastic process. The hybrid model we use is obtained by modifying the corresponding deterministic model, originally proposed by Kirschner and Panetta. We consider Adoptive Cellular Immunotherapies and Interleukin-based therapies, as well as their combination. By asymptotic and transitory analyses of the corresponding deterministic model we find conditions guaranteeing tumor eradication, and we tune the parameters of the hybrid model accordingly. We then perform stochastic simulations of the hybrid model under various therapeutic settings: constant, piece-wise constant or impulsive infusion and daily or weekly delivery schedules. CONCLUSIONS: Results suggest that, in some cases, the delivery schedule may deeply impact on the therapy-induced tumor eradication time. Indeed, our model suggests that Interleukin-based therapies may not be effective for every patient, and that the piece-wise constant is the most effective delivery to stimulate the immune-response. For Adoptive Cellular Immunotherapies a metronomic delivery seems more effective, as it happens for other anti-angiogenesis therapies and chemotherapies, and the impulsive delivery seems more effective than the piece-wise constant. The expected synergistic effects have been observed when the therapies are combined.

Spatiotemporal effects of a possible chemorepulsion of tumor cells by immune system effectors.

Tumor-immune system interplay is a landmark of tumor development, and chemotactic attraction of immune system cells towards a tumor is a landmark of immune control. Since tumor cells are capable of chemotactic and chemorepulsive motion, based on a number of analogies between the behavior of tumor cells and that of bacteria, and on the production of potentially chemorepulsive semaphorins by immune systems effectors, we propose here the possibility of chemorepulsion of tumor cells, elicited by chemicals produced by immune system effectors such as macrophages and cytotoxic T lymphocytes. To study the effects of this hypothesized phenomenon within the framework of the interplay of neoplasias with the innate and adaptive immune system, we appropriately extend two well-known models: the tumor-macrophage models by Owen and Sherratt [Owen and Sherratt, J. Theor. Biol., 1998] and the Matzavinos-Chaplain model of tumor-CTL interaction [Matzavinos, Chaplain and Kuznetsov, Math. Med. Biol., 2004]. Our simulations suggest that this mechanism might allow a faster expansion of tumors, and in the concluding remarks we envisage a new possible route of immunoevasion.

Feedback control of social distancing for COVID-19 via elementary formulae.

Social distancing has been enacted in order to mitigate the spread of COVID-19. Like many authors, we adopt the classic epidemic SIR model, where the infection rate is the control variable. Its differential flatness property yields elementary closed-form formulae for open-loop social distancing scenarios, where, for instance, the increase of the number of uninfected people may be taken into account. Those formulae might therefore be useful to decision makers. A feedback loop stemming from model-free control leads to a remarkable robustness with respect to severe uncertainties and mismatches. Although an identification procedure is presented, a good knowledge of the recovery rate is not necessary for our control strategy.

Prospective study on the dose distribution to the acoustic structures during postoperative 3D conformal radiotherapy for parotid tumors: dosimetric and audiometric aspects.

BACKGROUND AND PURPOSE: To analyze dose distribution in the hearing organ and to evaluate the dose effect on the hearing thresholds in patients treated with post-parotidectomy 3-dimensional conformal radiotherapy (3D-CRT). METHODS AND MATERIALS: A total of 17 patients received post-parotidectomy 3D-CRT (median dose: 63 Gy). The audiometric evaluation comprised pure tone audiometry and tympanometry performed before radiotherapy (RT) and 3, 6, and 24 months after RT. The ear structures were delineated on planning computer tomography scans. Mean and maximum doses were calculated and dose-volume histograms were plotted. RESULTS: Before RT, the median baseline audiometric thresholds were normal. At 3 months post-RT, 3 patients were diagnosed as having middle ear underpressure and/or effusion that resolved completely by 6 months. During 2-year follow-up, none of the ears showed perceptive hearing loss at speech frequencies. The mean doses at ipsilateral external auditory canal, mastoids cells, tympanic case, Eustachian tube, semicircular canals, and cochlea were 44.8 Gy, 39.0 Gy, 30.9 Gy, 33.0 Gy, 19.6 Gy, and 19.2 Gy, respectively. The doses to the contralateral ear were negligible, except for the Eustachian tube (up to 28.2 Gy). CONCLUSION: Post-parotidectomy 3D-CRT is associated with relatively low doses to the ear and the surrounding structures. Post-RT audiometry did not show any permanent (neither conductive nor perceptive) hearing impairment. Only in 3 patients were there signs of transient unilateral dysfunction of the Eustachian tube observed during the first few months after RT. Longer follow-up and larger patient series are warranted to confirm these preliminary findings.

The impact of vaccine side effects on the natural history of immunization programmes: an imitation-game approach.

When the incidence and prevalence of most common vaccine preventable childhood infectious diseases are constantly low, as is the case in many industrialized countries, the incidence of vaccine-associated side effects might become a key determinant in vaccine demand. We study an SIR transmission model with dynamic vaccine demand based on an imitation mechanism where the perceived risk of vaccination is modelled as a function of the incidence of vaccine side effects. The model shows some important differences compared to previous game dynamic models of vaccination, and allows noteworthy inferences as regards both the past and future lifetime of vaccination programmes. In particular it is suggested that a huge disproportion between the perceived risk of disease and vaccination is necessary in order to achieve high coverages. This disproportion is further increased in highly industrialised countries. Such considerations represent serious challenges for future vaccination programmes.

Three-dimensional conformal postoperative radiotherapy in patients with parotid tumors: 10 years' experience at the European Institute of Oncology.

AIMS AND BACKGROUND: Salivary gland malignancies are rare. The aim of our study was to investigate radiotherapy-related toxicity and clinical outcome in patients treated at our division with postoperative radiotherapy (pRT) for parotid tumors. METHODS AND STUDY DESIGN: Forty-three consecutive patients (32 with primary parotid tumors, 9 with parotid metastases and 2 with recurrent benign diseases) were retrospectively analyzed. RESULTS: The median follow-up was 28 months. Twenty and 5 patients had a follow-up longer than 2 and 5 years, respectively. Thirty-seven patients were alive and most of them (78%) were free from disease. The local and distant control rates were higher in patients with primary parotid tumors (94% and 87.5%) than in patients with parotid metastases (87.5% and 75%). Grade 3 radiotherapy-related acute toxicity of skin and mucosa was recorded in 20.9% and 28% of patients, respectively. Two patients (4.7%) had grade 4 skin toxicity. Late toxicity data were available for 33 (77%) patients. None of the patients developed severe (grade 3 and 4) late toxicity of soft tissues, skin or temporomandibular joints. CONCLUSIONS: Postoperative radiotherapy is a feasible treatment that was found to be effective mainly in patients with primary parotid tumors. Toxicity was acceptable but could probably be further reduced using more advanced radiotherapy techniques. Longer follow-up is required to achieve definitive results.

Dynamics of partially mitigated multi-phasic epidemics at low susceptible depletion: phases of COVID-19 control in Italy as case study.

To mitigate the harmful effects of the COVID-19 pandemic, world countries have resorted - though with different timing and intensities - to a range of interventions. These interventions and their relaxation have shaped the epidemic into a multi-phase form, namely an early invasion phase often followed by a lockdown phase, whose unlocking triggered a second epidemic wave, and so on. In this article, we provide a kinematic description of an epidemic whose time course is subdivided by mitigation interventions into a sequence of phases, on the assumption that interventions are effective enough to prevent the susceptible proportion to largely depart from 100% (or from any other relevant level). By applying this hypothesis to a general SIR epidemic model with age-since-infection and piece-wise constant contact and recovery rates, we supply a unified treatment of this multi-phase epidemic showing how the different phases unfold over time. Subsequently, by exploiting a wide class of infectiousness and recovery kernels allowing reducibility (either to ordinary or delayed differential equations), we investigate in depth a low-dimensional case allowing a non-trivial full analytical treatment also of the transient dynamics connecting the different phases of the epidemic. Finally, we illustrate our theoretical results by a fit to the overall Italian COVID-19 epidemic since March 2020 till February 2021 i.e., before the mass vaccination campaign. This show the abilities of the proposed model in effectively describing the entire course of an observed multi-phasic epidemic with a minimal set of data and parameters, and in providing useful insight on a number of aspects including e.g., the inertial phenomena surrounding the switch between different phases.