arXiv subjects

Hartmut H. K. Lentz

Publications and source records attributed to Hartmut H. K. Lentz.

8 recordsLinked to original sources

arXiv2022

Decoding the double trouble: A mathematical modelling of co-infection dynamics of SARS-CoV-2 and influenza-like illness

After the detection of coronavirus disease 2019 (Covid-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, Hubei Province, China in late December, the cases of Covid-19 have spiralled out around the globe. Due to the clinical similarity of Covid-19 with other flulike syndromes, patients are assayed for other pathogens of influenza like illness. There have been reported cases of co-infection amongst patients with Covid-19. Bacteria for example Streptococcus pneumoniae, Staphylococcus aureus, Klebsiella pneumoniae, Mycoplasma pneumoniae, Chlamydia pneumonia, Legionella pneumophila etc and viruses such as influenza, coronavirus, rhinovirus/enterovirus, parainfluenza, metapneumovirus, influenza B virus etc are identified as co-pathogens. In our current effort, we develop and analysed a compartmental based Ordinary Differential Equation (ODE) type mathematical model to understand the co-infection dynamics of Covid-19 and other influenza type illness. In this work we have incorporated the saturated treatment rate to take account of the impact of limited treatment resources to control the possible Covid-19 cases. As results, we formulate the basic reproduction number of the model system. Finally, we have performed numerical simulations of the co-infection model to examine the solutions in different zones of parameter space.

q-bio.PE↗

arXiv2022

The Diffusion Metrics of African Swine Fever in Wild Boar

To control African swine fever (ASF) efficiently, easily interpretable metrics of the outbreak dynamics are needed to plan and adapt the required measures. We found that the spread pattern of African Swine Fever cases in wild boar follows the mechanics of a diffusion process, at least in the early phase, for the cases that occurred in Germany. Following incursion into a previously unaffected area, infection disseminates locally within a naive and abundant wild boar population. Using real case data for Germany, we derive statistics about the time differences and distances between consecutive case reports. With the use of these statistics, we generate an ensemble of random walkers (continuous time random walks, CTRW) that resemble the properties of the observed outbreak pattern as one possible realization of all possible disease dissemination patterns. The trained random walker ensemble yields the diffusion constant, the affected area, and the outbreak velocity of early ASF spread in wild boar. These quantities are easy to interpret, robust, and may be generalized or adapted to different regions. Therefore, diffusion metrics can be useful descriptors of early disease dynamics and help facilitate efficient control of African Swine Fever.

q-bio.PE↗

arXiv2021

Modelling control strategies against Classical Swine Fever: influence of traders and markets using static and temporal networks in Ecuador

Classical swine fever (CSF) in Ecuador is prevalent since 1940, pig farming represents an important economic and cultural sector. Recently, the National Veterinary Service (NVS) has implemented individual identification of pigs, movement control and mandatory vaccination against CSF, looking for a future eradication. Our aim was to characterise the pig premises according to risk criteria, analyse the effect of random and targeted strategies to control CSF and consider the temporal development of the network. We used social network analysis (SNA), SIRS (susceptible, infected, recovered, susceptible) network modelling and temporal network analysis. The data set contained 751,003 shipments and 6 million pigs from 2017 to 2019. 165,593 premises were involved: 144,118 farms, 138 industrials, 21,337 traders, and 51 markets. On annual average, 124,976 premises (75%) received or sent one movement with 1.5 pigs, in contrast, 166 (0.01%) with 1,372 movements and 11,607 pigs. Simulations resulted in CSF mean prevalence of 29.93%; Targeted selection strategy reduced the prevalence to 3.3%, while 24% with random selection. Selection of high-risk premises in every province was the best strategy using available surveillance infrastructure. Notably, selecting 10 traders/markets reduced the CSF prevalence to 4%, evidencing their prime influence over the network. Temporal analysis showed an overestimation of 38% (causal fidelity) in the number of transmission paths; The steps to cross the network were 4.3 (average path length), but take approximately 233 days. In conclusion, surveillance strategies applied by the NVS could be more efficient to find cases, reduce the spread of diseases and enable the implementation of risk-based surveillance. To focus the efforts on target selection of high-risk premises, special attention should be given to markets/traders which proved similar disease spread potential.

physics.soc-ph↗

arXiv2021

Can a patchy model describe the potential spread of West Nile virus in Germany?

In 2018, West Nile Virus (WNV) was detected for the first time in Germany. Since the first detection, 36 human cases and 175 cases in horses and birds are detected. The transmission cycle of West Nile Virus includes birds and mosquitoes and -- as dead-end hosts -- people and horses. Spatial dissemination of the disease is caused by the movements of birds and mosquitoes. While the activity and movement of mosquitoes are depending mainly on temperature, in the birds there is a complex movement pattern caused by local birds and long range dispersal birds. To this end, we have developed a metapopulation network model framework to delineate the potential spatial distribution and spread of WNV across Germany as well as to evaluate the risk throughout our proposed network model. Our model facilitates the interconnection amongst the vector, local birds and long range dispersal birds contact networks. We have assumed different distance dispersal kernels models for the vector and avian populations with the intention to include short and long range dispersal. The model includes spatial variation of mosquito abundance and the movements to resemble the reality.

physics.bio-ph↗

arXiv2021

Ticks on the run: A mathematical model of Crimean-Congo Haemorrhagic Fever (CCHF)-key factors for transmission

Crimean-Congo haemorrhagic fever (CCHF) is a tick-borne zoonotic disease caused by the Crimean-Congo hemorrhagic fever virus (CCHFV). Ticks belonging to the genus \textit{Hyalomma} are the main vectors and reservoir for the virus. It is maintained in nature in an endemic vertebrate-tick-vertebrate cycle. CCHFV is prevalent in wide geographical areas including Asia, Africa, South-Eastern Europe and the Middle East. Over the last decade, several outbreaks of CCHFV have been observed in Europe, mainly in Mediterranean countries. Due to the high case/fatality ratio of CCHFV in human sometimes, it is of great importance for public health. Climate change and the invasion of CCHFV vectors in Central Europe suggest that the establishment of the transmission in Central Europe may be possible in future. We developed a compartment-based nonlinear Ordinary Differential Equation (ODE) system to model the disease transmission cycle including blood sucking ticks, livestock and human. Sensitivity analysis of the basic reproduction number $R_0$ shows that decreasing in the tick survival time is an efficient method to eradicate the disease. The model supports us in understanding the influence of different model parameters on the spread of CCHFV. Tick to tick transmission through co-feeding and the CCHFV circulation through trasstadial and transovarial stages are important factors to sustain the disease cycle. The proposed model dynamics are calibrated through an empirical multi-country analysis and multidimensional scaling reveals the disease-parameter sets of different countries burdened with CCHF are different. This necessary information may help us to select most efficient control strategies.

physics.bio-ph↗

arXiv2019

Contact-based model for epidemic spreading on temporal networks

We present a contact-based model to study the spreading of epidemics by means of extending the dynamic message passing approach to temporal networks. The shift in perspective from node- to edge-centric quantities enables accurate modelling of Markovian susceptible-infected-recovered outbreaks on time-varying trees, i.e., temporal networks with a loop-free underlying topology. On arbitrary graphs, the proposed contact-based model incorporates potential structural and temporal heterogeneities of the underlying contact network and improves analytic estimations with respect to the individual-based (node-centric) approach at a low computational and conceptual cost. Within this new framework, we derive an analytical expression for the epidemic threshold on temporal networks and demonstrate the feasibility of this method on empirical data.

physics.soc-ph↗

arXiv2016

Controlling contagious processes on temporal networks via adaptive rewiring

We consider recurrent contagious processes on a time-varying network. As a control procedure to mitigate the epidemic, we propose an adaptive rewiring mechanism for temporary isolation of infected nodes upon their detection. As a case study, we investigate the network of pig trade in Germany. Based on extensive numerical simulations for a wide range of parameters, we demonstrate that the adaptation mechanism leads to a significant extension of the parameter range, for which most of the index nodes (origins of the epidemic) lead to vanishing epidemics. Furthermore the performance of adaptation is very heterogeneous with respect to the index node. We quantify the success of the proposed adaptation scheme in dependence on the infectious period and the detection time. To support our findings we propose a mean-field analytical description of the problem.

physics.soc-ph↗

arXiv2016

Disease spread through animal movements: a static and temporal network analysis of pig trade in Germany

Background: Animal trade plays an important role for the spread of infectious diseases in livestock populations. As a case study, we consider pig trade in Germany, where trade actors (agricultural premises) form a complex network. The central question is how infectious diseases can potentially spread within the system of trade contacts. We address this question by analyzing the underlying network of animal movements. Methodology/Findings: The considered pig trade dataset spans several years and is analyzed with respect to its potential to spread infectious diseases. Focusing on measurements of network-topological properties, we avoid the usage of external parameters, since these properties are independent of specific pathogens. They are on the contrary of great importance for understanding any general spreading process on this particular network. We analyze the system using different network models, which include varying amounts of information: (i) static network, (ii) network as a time series of uncorrelated snapshots, (iii) temporal network, where causality is explicitly taken into account. Findings: Our approach provides a general framework for a topological-temporal characterization of livestock trade networks. We find that a static network view captures many relevant aspects of the trade system, and premises can be classified into two clearly defined risk classes. Moreover, our results allow for an efficient allocation strategy for intervention measures using centrality measures. Data on trade volume does barely alter the results and is therefore of secondary importance. Although a static network description yields useful results, the temporal resolution of data plays an outstanding role for an in-depth understanding of spreading processes. This applies in particular for an accurate calculation of the maximum outbreak size.

physics.soc-ph↗