Estimating unconfirmed COVID-19 infection cases and multiple waves of pandemic progression with consideration of testing capacity and non-pharmaceutical interventions: A dynamic spreading model

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has unique epidemiological characteristics that include presymptomatic and asymptomatic infections, resulting in a large proportion of infected cases being unconfirmed, including patients with clinical symptoms who have not been identified by screening. These unconfirmed infected individuals move and spread the virus freely, presenting difficult challenges to the control of the pandemic. To reveal the actual pandemic situation in a given region, a simple dynamic susceptible-unconfirmed-confirmed-removed (D-SUCR) model is developed taking into account the influence of unconfirmed cases, the testing capacity, the multiple waves of the pandemic, and the use of non-pharmaceutical interventions. Using this model, the total numbers of infected cases in 51 regions of the USA and 116 countries worldwide are estimated, and the results indicate that only about 40% of the true number of infections have been confirmed. In addition, it is found that if local authorities could enhance their testing capacities and implement a timely strict quarantine strategy after identifying the first infection case, the total number of infected cases could be reduced by more than 90%. Delay in implementing quarantine measures would drastically reduce their effectiveness.

Comparative Study of COVID-19 Pandemic Progressions in 175 Regions in Australia, Canada, Italy, Japan, Spain, U.K. and USA Using a Novel Model That Considers Testing Capacity and Deficiency in Confirming Infected Cases

Not identified as being exposed or infected, the group of asymptomatic and presymptomatic patients has become the key source of infectious hosts for the COVID-19 pandemic, triggering the re-emergence of outbreaks. Acknowledging the impacts of movement of unidentified patients and the limited testing capacity on understanding the spread of the virus, an augmented Susceptible-Exposed-Infectious-Confirmed-Recovered (SEICR) model integrating intercity migration data and testing capacity is developed to probe into the number of unidentified COVID-19 infected patients. This model allows evaluation of the effectiveness of active interventions, and more accurate prediction of the pandemic progression in a country, region or city. A pseudo-coevolutionary algorithm is adopted in the model fitting to provide an effective estimation of high-dimensional unknown parameter sets using a limited amount of historical data. The model is applied to 175 regions in Australia, Canada, Italy, Japan, Spain, the UK and USA to estimate the number of unconfirmed cases using limited historical data. Results showed that the actual number of infected cases could be 4.309 times as many as the official confirmed number. By implementing mass COVID-19 testing, the number of infected cases could be reduced by about 50%.

Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data

This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China were collected from Baidu Migration, a mobile-app based human migration tracking data system. Early outbreak data of infected, recovered and death cases from official source (from January 24 to February 16, 2020) were used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure was used for estimation of the dynamics of epidemic spreading in the following months. The work was completed on February 19, 2020. Our results showed that the number of infections in most cities in China would peak between mid February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively.

Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data

This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China were collected from Baidu Migration, a mobile-app based human migration tracking data system. Early outbreak data of infected, recovered and death cases from official source (from January 24 to February 16, 2020) were used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure was used for estimation of the dynamics of epidemic spreading in the following months. The work was completed on February 19, 2020. Our results showed that the number of infections in most cities in China would peak between mid February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively.

Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding

This work applies a data-driven coding method for prediction of the COVID-19 spreading profile in any given population that shows an initial phase of epidemic progression. Based on the historical data collected for COVID-19 spreading in 367 cities in China and the set of parameters of the augmented Susceptible-Exposed-Infected-Removed (SEIR) model obtained for each city, a set of profile codes representing a variety of transmission mechanisms and contact topologies is formed. By comparing the data of an early outbreak of a given population with the complete set of historical profiles, the best fit profiles are selected and the corresponding sets of profile codes are used for prediction of the future progression of the epidemic in that population. Application of the method to the data collected for South Korea, Italy and Iran shows that peaks of infection cases are expected to occur before mid April, the end of March and the end of May 2020, and that the percentage of population infected in each city or region will be less than 0.01%, 0.5% and 0.5%, for South Korea, Italy and Iran, respectively.

Advanced Algorithms for Local Routing Strategy on Complex Networks

Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets R_c increases by over ten-fold and the average transmission time 〈T〉 decreases by 70–90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks.

Traffic congestion in interconnected complex networks

Traffic congestion in isolated complex networks has been investigated extensively over the last decade. Coupled network models have recently been developed to facilitate further understanding of real complex systems. Analysis of traffic congestion in coupled complex networks, however, is still relatively unexplored. In this paper, we try to explore the effect of interconnections on traffic congestion in interconnected Barabási-Albert scale-free networks. We find that assortative coupling can alleviate traffic congestion more readily than disassortative and random coupling when the node processing capacity is allocated based on node usage probability. Furthermore, the optimal coupling probability can be found for assortative coupling. However, three types of coupling preferences achieve similar traffic performance if all nodes share the same processing capacity. We analyze interconnected Internet autonomous-system-level graphs of South Korea and Japan and obtain similar results. Some practical suggestions are presented to optimize such real-world interconnected networks accordingly.

A complex systems analysis of stick-slip dynamics of a laboratory fault

We study the stick-slip behavior of a granular bed of photoelastic disks sheared by a rough slider pulled along the surface. Time series of a proxy for granular friction are examined using complex systems methods to characterize the observed stick-slip dynamics of this laboratory fault. Nonlinear surrogate time series methods show that the stick-slip behavior appears more complex than a periodic dynamics description. Phase space embedding methods show that the dynamics can be locally captured within a four to six dimensional subspace. These slider time series also provide an experimental test for recent complex network methods. Phase space networks, constructed by connecting nearby phase space points, proved useful in capturing the key features of the dynamics. In particular, network communities could be associated to slip events and the ranking of small network subgraphs exhibited a heretofore unreported ordering.

An efficient and secure medical image protection scheme based on chaotic maps

Recently, the increasing demand for telemedicine services has raised interest in the use of medical image protection technology. Conventional block ciphers are poorly suited to image protection due to the size of image data and increasing demand for real-time teleradiology and other online telehealth applications. To meet this challenge, this paper presents a novel chaos-based medical image encryption scheme. To address the efficiency problem encountered by many existing permutation-substitution type image ciphers, the proposed scheme introduces a substitution mechanism in the permutation process through a bit-level shuffling algorithm. As the pixel value mixing effect is contributed by both the improved permutation process and the original substitution process, the same level of security can be achieved in a fewer number of overall rounds. The results indicate that the proposed approach provides an efficient method for real-time secure medical image transmission over public networks.

Characterizing chaotic dynamics from simulations of large strain behavior of a granular material under biaxial compression

For a given observed time series, it is still a rather difficult problem to provide a useful and compelling description of the underlying dynamics. The approach we take here, and the general philosophy adopted elsewhere, is to reconstruct the (assumed) attractor from the observed time series. From this attractor, we then use a black-box modelling algorithm to estimate the underlying evolution operator. We assume that what cannot be modeled by this algorithm is best treated as a combination of dynamic and observational noise. As a final step, we apply an ensemble of techniques to quantify the dynamics described in each model and show that certain types of dynamics provide a better match to the original data. Using this approach, we not only build a model but also verify the performance of that model. The methodology is applied to simulations of a granular assembly under compression. In particular, we choose a single time series recording of bulk measurements of the stress ratio in a biaxial compression test of a densely packed granular assembly-observed during the large strain or so-called critical state regime in the presence of a fully developed shear band. We show that the observed behavior may best be modeled by structures capable of exhibiting (hyper-) chaotic dynamics.

Attack resilience of the evolving scientific collaboration network

Stationary complex networks have been extensively studied in the last ten years. However, many natural systems are known to be continuously evolving at the local ("microscopic") level. Understanding the response to targeted attacks of an evolving network may shed light on both how to design robust systems and finding effective attack strategies. In this paper we study empirically the response to targeted attacks of the scientific collaboration networks. First we show that scientific collaboration network is a complex system which evolves intensively at the local level--fewer than 20% of scientific collaborations last more than one year. Then, we investigate the impact of the sudden death of eminent scientists on the evolution of the collaboration networks of their former collaborators. We observe in particular that the sudden death, which is equivalent to the removal of the center of the egocentric network of the eminent scientist, does not affect the topological evolution of the residual network. Nonetheless, removal of the eminent hub node is exactly the strategy one would adopt for an effective targeted attack on a stationary network. Hence, we use this evolving collaboration network as an experimental model for attack on an evolving complex network. We find that such attacks are ineffectual, and infer that the scientific collaboration network is the trace of knowledge propagation on a larger underlying social network. The redundancy of the underlying structure in fact acts as a protection mechanism against such network attacks.

Analysis, design, and control of a transcutaneous power regulator for artificial hearts

Based on a generic transcutaneous transformer model, a remote power supply using a resonant topology for use in artificial hearts is analyzed and designed for easy controllability and high efficiency. The primary and secondary windings of the transcutaneous transformer are positioned outside and inside the human body, respectively. In such a transformer, the alignment and gap may change with external positioning. As a result, the coupling coefficient of the transcutaneous transformer is also varying, and so are the two large leakage inductances and the mutual inductance. Resonant-tank circuits with varying resonant-frequency are formed from the transformer inductors and external capacitors. For a given range of coupling coefficients, an operating frequency corresponding to a particular coupling coefficient can be found, for which the voltage transfer function is insensitive to load. Prior works have used frequency modulation to regulate the output voltage under varying load and transformer coupling. The use of frequency modulation may require a wide control frequency range which may extend well above the load insensitive frequency. In this paper, study of the input-to-output voltage transfer function is carried out, and a control method is proposed to lock the switching frequency at just above the load insensitive frequency for optimized efficiency at heavy loads. Specifically, operation at above resonant of the resonant circuits is maintained under varying coupling-coefficient. Using a digital-phase-lock-loop (PLL), zero-voltage switching is achieved in a full-bridge converter which is also programmed to provide output voltage regulation via pulsewidth modulation (PWM). A prototype transcutaneous power regulator is built and found to to perform excellently with high efficiency and tight regulation under variations of the alignment or gap of the transcutaneous transformer, load and input voltage.

Scale-free user-network approach to telephone network traffic analysis

The effect of the user network on the telephone network traffic is studied in this paper. Unlike classical traffic analysis, where users are assumed to be connected uniformly, our proposed method employs a scale-free network to model the behavior of telephone users. Each user has a fixed set of acquaintances with whom the user may communicate, and the number of acquaintances follows a power-law distribution. We show that compared to conventional analysis based upon a fully connected user network, the network traffic is significantly different when the user network assumes a scale-free property. Specifically, network blocking (call failure) is generally more severe in the case of a scale-free user network. It is also shown that the carried traffic is practically limited by the scale-free property of the user network, rather than by the network capacity.

Clustering model for transmission of the SARS virus: application to epidemic control and risk assessment

We propose a new four state model for disease transmission and illustrate the model with data from the 2003 SARS epidemic in Hong Kong. The critical feature of this model is that the community is modelled as a small-world network of interconnected nodes. Each node is linked to a fixed number of immediate neighbors and a random number of geographically remote nodes. Transmission can only propagate between linked nodes. This model exhibits two features typical of SARS transmission: geographically localized outbreaks and "super-spreaders". Neither of these features are evident in standard susceptible-infected-removed models of disease transmission. Our analysis indicates that "super-spreaders" may occur even if the infectiousness of all infected individuals is constant. Moreover, we find that nosocomial transmission in Hong Kong directly contributed to the severity of the outbreak and that by limiting individual exposure time to 3-5 days the extent of the SARS epidemic would have been minimal.

Minimum description length neural networks for time series prediction

Artificial neural networks (ANN) are typically composed of a large number of nonlinear functions (neurons) each with several linear and nonlinear parameters that are fitted to data through a computationally intensive training process. Longer training results in a closer fit to the data, but excessive training will lead to overfitting. We propose an alternative scheme that has previously been described for radial basis functions (RBF). We show that fundamental differences between ANN and RBF make application of this scheme to ANN nontrivial. Under this scheme, the training process is replaced by an optimal fitting routine, and overfitting is avoided by controlling the number of neurons in the network. We show that for time series modeling and prediction, this procedure leads to small models (few neurons) that mimic the underlying dynamics of the system well and do not overfit the data. We apply this algorithm to several computational and real systems including chaotic differential equations, the annual sunspot count, and experimental data obtained from a chaotic laser. Our experiments indicate that the structural differences between ANN and RBF make ANN particularly well suited to modeling chaotic time series data.

Polymorphisms in the DNA repair gene XRCC1 and breast cancer

X-ray repair cross complementing group 1 (XRCC1) encodes a protein involved in base excision repair. We examined the association of polymorphisms in XRCC1 (codon 194 Arg-->Trp and codon 399 Arg-->Gln) and breast cancer in the Carolina Breast Cancer Study, a population-based case-control study in North Carolina. No association was observed between XRCC1 codon 194 genotype and breast cancer, and odds ratios (ORs) were not modified by smoking or radiation exposure. A positive association for XRCC1 codon 399 Arg/Gln or Gln/Gln genotypes compared with Arg/Arg was found among African Americans (253 cases, 266 controls; OR = 1.7, 95% confidence interval, 1.1-2.4) but not whites (386 cases, 381 controls; OR =1.0, 95% confidence interval, 0.8-1.4). Among African-American women, ORs for the duration of smoking were elevated among women with XRCC1 codon 399 Arg/Arg genotype (trend test; P < 0.001) but not Arg/Gln or Gln/Gln (P = 0.23). There was no difference in OR for smoking according to XRCC1 codon 399 genotype in white women. ORs for occupational exposure to ionizing radiation were stronger for African-American and white women with codon 399 Arg/Arg genotype. High-dose radiation to the chest was more strongly associated with breast cancer among white women with XRCC1 codon 399 Arg/Arg genotype. Our results suggest that XRRC1 codon 399 genotype may influence breast cancer risk, perhaps by modifying the effects of environmental exposures. However, interpretation of our results is limited by incomplete knowledge regarding the biological function of XRCC1 alleles.

On-line adaptive chaotic demodulator based on radial-basis-function neural networks

Chaotic modulation is a useful technique for spread spectrum communication. In this paper, an on-line adaptive chaotic demodulator based on a radial-basis-function (RBF) neural network is proposed and designed. The demodulator is implemented by an on-line adaptive learning algorithm, which takes advantage of the good approximation capability of the RBF network and the tracking ability of the extended Kalman filter. It is demonstrated that, provided the modulating parameter varies slowly, spread spectrum signals contaminated by additive white Gaussian noise in a channel can be tracked in a time window, and the modulating parameter, which carries useful messages, can be estimated using the least-square fit. The Henon map is chosen as the chaos generator. Four test message signals, namely, square-wave, sine-wave, speech and image signals, are used to evaluate the performance. The results verify the ability of the demodulator in tracking the dynamics of the chaotic carrier as well as retrieving the message signal from a noisy channel.

Plasma 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene (DDE) levels and immune response

For determination of whether plasma 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene (DDE) pesticide levels (< or =1-32 ppb) are associated with immune suppression or DNA damage in lymphocytes, 302 individuals residing in Moore County, North Carolina, in 1994-1996 provided a blood specimen, underwent a skin test, and answered a questionnaire concerning factors affecting plasma organochlorine pesticide levels and the immune system. The blood specimens were analyzed for levels of plasma DDE (a metabolite of 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane), numbers and types of blood cells, immunoglobulin levels, mitogen-induced lymphoproliferative activity, and lymphocyte micronuclei. When DDE levels were categorized as 1 or less, more than 1 to 2, more than 2 to 4.3, more than 4.3 to 7.6, and more than 7.6 ppb, individuals with higher plasma DDE levels had lowered mitogen-induced lymphoproliferative activity (concanavalin A, range: 74,218 dropping to 55,880 counts per minute, p = 0.03) and modestly increased total lymphocytes (range: 2.0-2.3 x 10(3)/microl, p = 0.05) and immunoglobulin A levels (range: 210-252 mg/dl, p = 0.04). There were no consistent differences in response to the skin tests by plasma DDE levels. Plasma DDE levels were not associated with a higher frequency of micronuclei. The authors conclude that relatively low levels of plasma DDE are associated with statistically significant changes in immune markers, although the magnitude of the effects are of uncertain clinical importance.

Effects on the immune system associated with living near a pesticide dump site

In this paper, we report results of the second phase of a larger study designed to evaluate the effects on the immune system of living near a Superfund site containing organochlorine pesticides, volatile organic compounds, and metals. Phase II was conducted to determine whether living near the site, consisting of six locations in Aberdeen, North Carolina, is associated with higher plasma organochlorine levels, immune suppression, or DNA damage. Each of 302 residents of Aberdeen and neighboring communities provided a blood specimen, underwent a skin test, and answered a questionnaire. Blood specimens were analyzed for organochlorine pesticides, immune markers, and micronuclei. Of 20 organochlorines tested, only DDE was detected in the blood of participants (except for one individual). Age-adjusted mean plasma DDE levels were 4.05 ppb for Aberdeen residents and 2.95 ppb (p = 0.01) for residents of neighboring communities. Residents of 40-59 years of age who lived within a mile of any site, but particularly the Farm Chemicals site, had higher plasma DDE levels than residents who lived farther away. Residents who lived near the Farm Chemicals site before versus after 1985 also had higher plasma DDE levels. Overall, there were few differences in immune markers between residents of Aberdeen and the neighboring communities. However, residents who lived closer to the dump sites had statistically significantly lower mitogen-induced lymphoproliferative activity than residents who lived farther away (p < 0.05). Residential location was not consistently associated with frequency of micronuclei or skin test responses. Although some statistically significant differences in immune markers were noted in association with residential location, the magnitude of effects are of uncertain clinical importance.

Dichlorodiphenyldichloroethene, polychlorinated biphenyls, and breast cancer among African-American and white women in North Carolina

We examined plasma dichlorodiphenyldichloroethene (DDE) and total polychlorinated biphenyl (PCB) levels in relation to breast cancer in a population-based, case-control study of African-American women (292 cases and 270 controls) and white women (456 cases and 389 controls) in North Carolina. Adjusted odds ratios (ORs) for breast cancer comparing the highest to lowest third of DDE were 1.41 [95% confidence interval (CI), 0.87-2.29] in African-American women and 0.98 (95% CI, 0.67-1.43) in white women. ORs comparing the highest to lowest third of total PCBs were 1.74 (95% CI, 1.00-3.01) in African-American women and 1.03 (95% CI, 0.68-1.56) in white women. Among African-Americans, the OR for total PCBs was highest for obese women (body mass index 234.2; OR, 4.92; 95% CI, 1.63-14.83). In contrast, the OR for DDE was highest for the leanest African-American women (body mass index, <25; OR, 3.84; 95% CI, 0.98-15.08). ORs for DDE were not elevated among women who lived or worked on farms or elevated among farming women who reported exposure to pesticides. Our results suggest absence of a strong effect for DDE or total PCBs in breast cancer but lend support for associations among subgroups of women. In our study, factors such as income, parity, breastfeeding, race/ethnicity, and body mass index influenced the relationship of organochlorines and breast cancer. Differing distributions of such factors may explain some of the inconsistencies across previous studies.