arXiv subjects

Marcos Amaku

Publications and source records attributed to Marcos Amaku.

11 recordsLinked to original sources

arXiv2026

A New Method to Predict the Effect of an Intervention in the Host Population to Reduce the Magnitude of an Outbreak of a Vector-Borne Infection

In this paper, we propose a new model to estimate the impact of an intervention on human hosts of a vector-borne infection, such as dengue, which occurs in yearly outbreaks of different magnitudes. The model applies to these outbreaks and, in fact, is independent of their intensity, that is, it does not require the steady-state assumption. The model takes as input the officially reported age-dependent number of cases of a vector-borne infection. It is deterministic and does not account for stochasticity. Our objective is to estimate the impact of the intervention (the efficacy), and we rely on the observed fact that the age distribution of the proportion of cases of the infections transmitted by the same vector is independent of both the intensity of transmission and the geographic area studied, at least for Brazilian regions. This finding is highlighted in the main text and forms the basis of our calculations. A hypothetical intervention is simulated using a dengue vaccine, which allows the determination of the optimal strategy for a vaccination campaign.

q-bio.PE↗

arXiv2026

Age-dependent distribution of officially reported cases of vector-borne infections

OBJECTIVE: To propose a new approach to analyze the age-distribution of reported cases for vector-transmitted infections. METHODS: Using officially reported number of cases of dengue, Zika, chikungunya, malaria and leishmaniasis for distinct geographical areas, in different periods. Data were treated in special but well-known procedure, transforming the raw data into a density age-dependent distribution and fitting a special continuous function to it. RESULTS: We found that the proportion of age-dependent cases with respect to the total number of cases in a given year (or any transmission season) is probably determined by the ecological interactions between vectors and hosts. The age-distribution of the proportion of cases for the three Aedes-related infections are essentially the same independently of the magnitude of the outbreak and the geographical region considered. On the other hand, for the infections transmitted by other vectors, the age-distributions of the proportion of cases are entirely different. CONCLUSIONS: During specific outbreaks, the ratio between the age distribution of the proportion of officially reported cases and the total number of cases for Aedes transmitted infections such as dengue, chikungunya and zika is independent of the size of the outbreak, the size of the studied population, the period when the outbreak occurs; and the geographical region considered. Our results also suggest that the age-distribution of cases is mainly due to the interaction between vectors and their hosts.

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↗

arXiv2017

Estimating the Size of Aedes aegypti Populations from Dengue Incidence Data: Implications for the Risk of Yellow Fever, Zika Virus and Chikungunya Outbreaks

In this paper we present a model to estimate the density of aedes mosquitoes in a community affected by dengue. The model is based on the fitting of a continuous function to the incidence of dengue infections, from which the density of infected mosquitoes is derived straightforwardly. Further derivations allows the calculation of the latent and susceptible mosquitoes' densities, the sum of the three equals the total mosquitoes' density. The model is illustrated with the case of the risk of urban yellow fever resurgence in dengue infested areas but the same methods apply for other aedes-transmitted infections like Zika and chikungunya viruses.

q-bio.PE↗

arXiv2017

Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databases

Background: National or local laws, norms or regulations (sometimes and in some countries) require medical providers to report notifiable diseases to public health authorities. Reporting, however, is almost always incomplete. This is due to a variety of reasons, ranging from not recognizing the diseased to failures in the technical or administrative steps leading to the final official register in the disease notification system. The reported fraction varies from 9% to 99% and is strongly associated with the disease being reported. Methods: In this paper we propose a method to approximately estimate the full prevalence (and any other variable or parameter related to transmission intensity) of infectious diseases. The model assumes incomplete notification of incidence and allows the estimation of the non-notified number of infections and it is illustrated by the case of hepatitis C in Brazil. The method has the advantage that it can be corrected iteratively by comparing its findings with empirical results. Results: The application of the model for the case of hepatitis C in Brazil resulted in a prevalence of notified cases that varied between 163,902 and 169,382 cases; a prevalence of non-notified cases that varied between 1,433,638 and 1,446,771; and a total prevalence of infections that varied between 1,597,540 and 1,616,153 cases. Conclusions: We conclude that that the model proposed can be useful for estimation of the actual magnitude of endemic states of infectious diseases, particularly for those where the number of notified cases is only the tip of the iceberg. In addition, the method can be applied to other situations, such as the well known underreported incidence of criminality (for example rape), among others.

q-bio.PE↗

arXiv2016

The Magnitude and Frequency Variations of Vector-Borne Infections Outbreaks with the Ross-Macdonald Model: Explaining and Predicting Outbreaks of Dengue Fever

It is possible to model vector-borne infection using the classical Ross-Macdonald model. This attempt, however fails in several respects. First, using measured (or estimated) parameters, the model predicts a much greater number of cases than what is usually observed. Second, the model predicts a single huge outbreaks that is followed after decades of much smaller outbreaks. This is not what is observed. Usually towns or cities report a number of cases that recur for many years, even when environmental changes cannot explain the disappearance of the infection in-between the peaks. In this paper we continue to examine the pitfalls in modeling this class of infections, and explain that, in fact, if properly used, the Ross-Macdonald model works, can be used to understand the patterns of epidemics and even, to some extents, to make some predictions. We model several outbreaks of dengue fever and show that the variable pattern of year recurrence (or absence of it) can be understood and explained by a simple Ross-Macdonald model modified to take into account human movement across a range of neighborhoods inside a city. In addition, we analyze the effect of seasonal variations in the parameters determining the number, longevity and biting behavior of mosquitoes. Based on the size of the first outbreak, we show that it is possible to estimate the proportion of the remaining susceptibles and predict the likelihood and magnitude of eventual subsequent outbreaks. The approach is exemplified by actual dengue outbreaks with different recurrence patterns from some Brazilian regions.

q-bio.PE↗

arXiv2015

Caracterização de circuitos pecuários com base em redes de movimentação de animais

A network is a set of nodes that are linked together by a set of edges. Networks can represent any set of objects that have relations among themselves. Communities are sets of nodes that are related in an important way, probably sharing common properties and/or playing similar roles within a network. When network analysis is applied to study the livestock movement patterns, the epidemiological units of interest (farm premises, counties, states, countries, etc.) are represented as nodes, and animal movements between the nodes are represented as the edges of a network. Unraveling a network structure, and hence the trade preferences and pathways, could be very useful to a researcher or a decision-maker. We implemented a community detection algorithm to find livestock communities that is consistent with the definition of a livestock production zone, assuming that a community is a group of farm premises in which an animal is more likely to stay during its life time than expected by chance. We applied this algorithm to the network of within animal movements made inside the State of Mato Grosso, for the year of 2007. This database holds information about 87,899 premises and 521,431 movements throughout the year, totalizing 15,844,779 animals moved. The community detection algorithm achieved a network partition that shows a clear geographical and commercial pattern, two crucial features to preventive veterinary medicine applications, and also has a meaningful interpretation in trade networks where links emerge from the choice of trader nodes.

q-bio.QM↗

arXiv2014

The friendship paradox in scale-free networks

Our friends have more friends than we do. That is the basis of the friendship paradox. In mathematical terms, the mean number of friends of friends is higher than the mean number of friends. In the present study, we analyzed the relationship between the mean degree of vertices (individuals), , and the mean number of friends of friends, , in scale-free networks with degrees ranging from a minimum degree (k_min) to a maximum degree (k_max). We deduced an expression for - for scale-free networks following a power-law distribution with a given scaling parameter (alpha). Based on this expression, we can quantify how the degree distribution of a scale-free network affects the mean number of friends of friends.

cs.SI↗

arXiv2013

Modeling the Dynamics of Infectious Diseases in Different Scale-Free Networks with the Same Degree Distribution

The transmission dynamics of some infectious diseases is related to the contact structure between individuals in a network. We used five algorithms to generate contact networks with different topological structure but with the same scale-free degree distribution. We simulated the spread of acute and chronic infectious diseases on these networks, using SI (Susceptible - Infected) and SIS (Susceptible - Infected - Susceptible) epidemic models. In the simulations, our objective was to observe the effects of the topological structure of the networks on the dynamics and prevalence of the simulated diseases. We found that the dynamics of spread of an infectious disease on different networks with the same degree distribution may be considerably different.

q-bio.PE↗

arXiv2013

Scale-Free Networks with the Same Degree Distribution: Different Structural Properties

We have analysed some structural properties of scale-free networks with the same degree distribution. Departing from a degree distribution obtained from the Barabási-Albert (BA) algorithm, networks were generated using four additional different algorithms a (Molloy-Reed, Kalisky, and two new models named A and B) besides the BA algorithm itself. For each network, we have calculated the following structural measures: average degree of the nearest neighbours, central point dominance, clustering coefficient, the Pearson correlation coefficient, and global efficiency. We found that different networks with the same degree distribution may have distinct structural properties. In particular, model B generates decentralized networks with a larger number of components, a smaller giant component size, and a low global efficiency when compared to the other algorithms, especially compared to the centralized BA networks that have all vertices in a single component, with a medium to high global efficiency. The other three models generate networks with intermediate characteristics between B and BA models. A consequence of this finding is that the dynamics of different phenomena on these networks may differ considerably.

cs.SI↗

arXiv2013

A Comparative Analysis of the Relative Efficacy of Vector-Control Strategies against Dengue Fever

The model considers the human population, the adult mosquito population and the population of immature stages, which includes eggs, larvae and pupae. The model also considers the vertical transmission of dengue in the mosquitoes and the seasonal variation in the mosquito population. From this basic model describing the dynamics of dengue infection, we deduce thresholds for avoiding the introduction of the disease and for the elimination of the disease. In particular, we deduce a Basic Reproduction Number for dengue that includes parameters related to the immature stages of the mosquito. By neglecting seasonal variation, we calculate the equilibrium values of the models variables. We also present a sensitivity analysis of the impact of four vector-control strategies on the Basic Reproduction Number and on the Force of Infection of dengue. Each of the strategies was studied separately from the others. The analysis presented allows us to conclude that of the available vector control strategies, adulticide application is the most effective, followed by the reduction of the exposure to mosquito bites, locating and destroying breeding places and, finally, larvicides. Current vector-control methods are concentrated in mechanical destruction of mosquitoes breeding places. Our results suggest that reducing the contact between vector and hosts (biting rates) are as efficient as the logistically difficult but very efficient adult mosquitos control.

math.DS↗